1 Anotações de coisas por fazer:

  • Descobrir como colocar as estações no sentido correto montante -> jusante nos sumários
  • not entirely necessary pq tenho os sumários mais aprimorados ainda no excel.

87398500, 87398980, 87398900, 87398950, 87405500, 87406900, 87409900

  • Aprender a segmentar o meu dataset por períodos
  • aprender a criar uma nova coluna com a segmentação dos períodos
  • aprender a colocar a legenda dentro do gráfico
    • reduzir o tamanho da legenda
  • corrigir os valores 0 de IQA pra NA
  • descobrir como conseguir a equação do lm
  • aprender a pivotar o sumário -> meu sumário do google docs ta batendo direitinho com o do R
  • descobrir se há outros TCCs com disponibilização de códigos
  • correlação forte entre condutividade e Namon/Ptot/DBO
1990-2000 2000-2010 2010-2020
1990-2000 2000-2010 2010-2020

2 Instalar os pacotes

# install.packages(tidyverse)

2.1 acessar os pacotes

# library(readr)
# library(rmarkdown)
# # library(qboxplot)
# library(readxl)
# library(pillar)
# library(dplyr)
# library(tidyverse)
# library(gapminder)
# library(knitr)
# library(kableExtra)
# library(ggpubr)
# library(gridExtra)
# library(modelsummary)
# library(gtsummary)
# library(GGally)
pacman::p_load(readr, rmarkdown, readxl,
               pillar, dplyr, tidyverse,
               gapminder, knitr, kableExtra,
               gridExtra, #modelsummary, 
               gtsummary, ggplot2,
               ggbeeswarm, GGally)
# pacman::p_load(tibbletime)
knitr::knit_hooks$set(time_it = local({
  now <- NULL
  function(before, options) {
    if (before) {
      # record the current time before each chunk
      now <<- Sys.time()
    } else {
      # calculate the time difference after a chunk
      res <- difftime(Sys.time(), now)
      # return a character string to show the time
      paste("Time for this code chunk to run:", res)
    }
  }
}))

knitr::opts_chunk$set(time_it = TRUE)

2.2 importando a planilha

3 data wrangling

plan_wide_19902020 <- plan_wide_19902020 %>% 
  mutate(IQA = ifelse(IQA == 0, NA, IQA))

4 setting theme

theme_grafs <- function(bg = "white", 
                        coloracao_letra = "black") {
  theme(
    plot.title = element_text(
      hjust = 0.5,
      color = coloracao_letra,
      size = 19),
    
    axis.title.x = 
      # element_text(
      # color = coloracao_letra,
      # size = 15,
      # angle = 0,),
      element_blank(),
    axis.title.y = element_text(
      color = coloracao_letra,
      size = 15,
      angle = 90),
    
    axis.text.x = element_text(
      color = coloracao_letra,
      size = 17),
    axis.text.y = element_text(
      color = coloracao_letra,
      size = 17,
      angle = 0),
    
    panel.background = element_rect(fill = bg),
    plot.background = element_rect(fill = bg),
    plot.margin = margin(l = 5, r = 10,
                         b = 5, t = 5)
  )
}

5 setting periodos

# p1 <- plan_wide_19902020 %>% 
#   filter(ANO_COLETA > "1990" &
#            ANO_COLETA <= "2000")
# 
# p2 <- plan_wide_19902020 %>% 
#   filter(ANO_COLETA > "2000" &
#            ANO_COLETA <= "2010")
# 
# p3 <- plan_wide_19902020 %>% 
#   filter(ANO_COLETA > "2010" &
#            ANO_COLETA <= "2020")

# teste_all_periodos <- plan_wide_19902020 %>% 
#   filter(
#     between(ANO_COLETA, 1990, 2000)
#   )

6 setting sumaries

p1 <- plan_wide_19902020 %>% 
  filter(ANO_COLETA > "1990" &
           ANO_COLETA <= "2000")

p2 <- plan_wide_19902020 %>%
  filter(ANO_COLETA > "2000" &
         ANO_COLETA <= "2010")

p3 <- plan_wide_19902020 %>%
  filter(ANO_COLETA > "2010" &
         ANO_COLETA <= "2020")

  # periodo = c(p1 <- plan_wide_19902020 %>% 
  #   filter(ANO_COLETA > "1990" &
  #            ANO_COLETA <= "2000"),
  # 
  # p2 <- plan_wide_19902020 %>%
  #   filter(ANO_COLETA > "2000" &
  #            ANO_COLETA <= "2010"),
  # 
  # p3 <- plan_wide_19902020 %>%
  #   filter(ANO_COLETA > "2010" &
  #            ANO_COLETA <= "2020"))

# sumario <- function(parametros = parametros, periodo){
#   plan_wide_19902020 %>%
#    select(CODIGO, ., ANO_COLETA) %>% 
#    # filter(ANO_COLETA>"1990" &
#    #          ANO_COLETA<="2000") %>% 
#    group_by(CODIGO) %>% 
#    summarize(
#      min = 
#        min(parametros, 
#            na.rm = TRUE),
#      q1 = 
#        quantile(parametros, 0.25, 
#                 na.rm = TRUE),
#      median = 
#        median(parametros, 
#               na.rm = TRUE),
#      mean = 
#        mean(parametros, 
#             na.rm= TRUE),
#      q3 = 
#        quantile(parametros, 0.75, 
#                 na.rm = TRUE),
#      max = 
#        max(parametros, 
#            na.rm = TRUE))
# }

# plan_wide_19902020 %>% 
#   sumario(parametros = DBO)

# sum_IQA_p1 <- plan_wide_19902020 %>%
#    select(CODIGO, IQA, ANO_COLETA) %>% 
#    filter(ANO_COLETA>"1990" &
#             ANO_COLETA<="2000") %>% 
#    group_by(CODIGO) %>% 
#    summarize(
#      min = 
#        min(IQA, 
#            na.rm = TRUE),
#      q1 = 
#        quantile(IQA, 0.25, 
#                 na.rm = TRUE),
#      median = 
#        median(IQA, 
#               na.rm = TRUE),
#      mean = 
#        mean(IQA, 
#             na.rm= TRUE),
#      q3 = 
#        quantile(IQA, 0.75, 
#                 na.rm = TRUE),
#      max = 
#        max(IQA, 
#            na.rm = TRUE))

7 Parâmetros físico-químicos

7.0.1 Oxigênio Dissolvido

# par_od <- plan_wide_19902020 %>% 
#   select(CODIGO, `Oxigênio dissolvido`) %>% 
#   group_nest(CODIGO)

# parametros_IQA

# parametros <- colnames(parametros_IQA)[]

plan_wide_19902020 %>%
  ggplot(
    aes(CODIGO, `Oxigênio dissolvido`)
  )+
  annotate("rect",
           xmin = -Inf, xmax = Inf,
           ymin = -Inf, ymax = 2,
           alpha = 1,
           fill = "#ac5079")+ # >pior classe
  annotate("rect",
           xmin = -Inf, xmax = Inf,
           ymin = 2, ymax = 4,
           alpha = 1,
           fill = "#eb5661")+ #classe 4
  annotate("rect",
           xmin = -Inf, xmax = Inf,
           ymin = 4, ymax = 5,
           alpha=1,
           fill="#fcf7ab")+ #classe 3
  annotate("rect",
           xmin=-Inf,
           xmax=Inf,
           ymin=5,
           ymax=6,
           alpha=1,
           fill="#70c18c")+ #classe 2
  annotate("rect",
           xmin=-Inf,
           xmax=Inf,
           ymin=6,
           ymax=Inf,
           alpha=1,
           fill="#8dcdeb")+ #classe 1
  stat_boxplot(
    geom = 'errorbar',
    width=0.3,
    position = position_dodge(width = 0.65)
  )+
  geom_boxplot(
    fill = '#F8F8FF',
    color = "black",
    outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
    width= 0.7
  )+
  labs(
    title = "Oxigênio Dissolvido no período 1990-2000",
    x = "Estação",
    y = "mg/L"
  )+
  ggbeeswarm::geom_quasirandom(
    size = 1.2,
    alpha = .25,
    width = .07,
  )+
  scale_y_continuous(
    expand = expansion(mult = c(0,0)),
    n.breaks = 11,
    limits = c(-1,21)
  )+
  scale_x_discrete(limits = c("87398500",
                              "87398980",
                              "87398900",
                              "87398950",
                              "87405500",
                              "87406900",
                              "87409900"),
                   labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
  )+
  geom_smooth(method = "lm",
              se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
              aes(group=1),
              alpha=.5,
              na.rm = TRUE,
              size = 1)
## Warning: Removed 92 rows containing non-finite values (stat_boxplot).
## Removed 92 rows containing non-finite values (stat_boxplot).
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 92 rows containing missing values (position_quasirandom).

Oxigênio Dissolvido no período 1990-2000

grid.arrange(od_p1, od_p2, od_p3, ncol = 3)

Oxigênio Dissolvido no período 1990-2020

ggsave("od_p1.png",
       plot = od_p1,
       path = "./graficos",
       dpi = 300,
       type = "cairo")
## Saving 10 x 6.66 in image
## Warning: Using ragg device as default. Ignoring `type` and `antialias` arguments
## Warning: Removed 7 rows containing non-finite values (stat_boxplot).
## Removed 7 rows containing non-finite values (stat_boxplot).
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 7 rows containing missing values (position_quasirandom).
ggsave("od_p2.png",
       plot = od_p2,
       path = "./graficos",
       dpi = 300,
       type = "cairo")
## Saving 10 x 6.66 in image
## Warning: Using ragg device as default. Ignoring `type` and `antialias` arguments
## Warning: Removed 54 rows containing non-finite values (stat_boxplot).
## Removed 54 rows containing non-finite values (stat_boxplot).
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 54 rows containing missing values (position_quasirandom).
ggsave("od_p3.png",
       plot = od_p3,
       path = "./graficos",
       dpi = 300,
       type = "cairo")
## Saving 10 x 6.66 in image
## Warning: Using ragg device as default. Ignoring `type` and `antialias` arguments
## Warning: Removed 31 rows containing non-finite values (stat_boxplot).
## Removed 31 rows containing non-finite values (stat_boxplot).
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 31 rows containing missing values (position_quasirandom).
ggsave("od_3periodos_2.png",
       units = c("px"),
       width = 4500,
       height = 2993,
       plot = grid.arrange(od_p1, od_p2, od_p3, ncol = 3),
       path = "./graficos",
       dpi = 300,
       type = "cairo")
## Warning: Removed 7 rows containing non-finite values (stat_boxplot).
## Warning: Removed 7 rows containing non-finite values (stat_boxplot).
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 7 rows containing missing values (position_quasirandom).
## Warning: Removed 54 rows containing non-finite values (stat_boxplot).
## Removed 54 rows containing non-finite values (stat_boxplot).
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 54 rows containing missing values (position_quasirandom).
## Warning: Removed 31 rows containing non-finite values (stat_boxplot).
## Removed 31 rows containing non-finite values (stat_boxplot).
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 31 rows containing missing values (position_quasirandom).
## Warning: Using ragg device as default. Ignoring `type` and `antialias` arguments

grid.arrange(iqaod_p1, iqaod_p2, iqaod_p3, ncol = 3)

Resultado que quero chegar pro sumário

variable Estação 1 Estação 2 Estação 3 Estação 4
max 15 17 16 14
med 14 16 15 13
min 13 15 14 12
n 15 12 3 4
## # A tibble: 7 x 7
##   CODIGO     min    q1 median  mean    q3   max
##   <chr>    <dbl> <dbl>  <dbl> <dbl> <dbl> <dbl>
## 1 87398500   0.8  4.9    6.4   5.99  7.3   10.8
## 2 87398900   2    5.6    6.9   6.78  8     10.5
## 3 87398950   2.5  4.4    5.95  5.98  7.1   10.3
## 4 87398980   4.2  6      6.3   7.01  8.2   12.1
## 5 87405500   0.1  1.9    4.2   4.22  6     19.9
## 6 87406900   0.1  0.25   2.6   2.98  5     10.2
## 7 87409900   0.1  1.4    2.9   3.60  5.65  11.1
## # A tibble: 7 x 7
##   CODIGO     min    q1 median  mean    q3   max
##   <chr>    <dbl> <dbl>  <dbl> <dbl> <dbl> <dbl>
## 1 87398500   0.4   3.5   4.9   5.01  6.65  10.9
## 2 87398900   1.9   4     5.5   5.33  6.6   12  
## 3 87398950   1.7   3.2   5.3   5.06  6.18   8.9
## 4 87398980   1.2   3.8   5.6   5.38  6.6    9.2
## 5 87405500   0.2   1.4   2.55  3.28  4     14.2
## 6 87406900   0     1.1   1.9   2.59  3.15  16  
## 7 87409900   0     0.7   2.3   3.12  3.7   10.6
## # A tibble: 7 x 7
##   CODIGO     min    q1 median  mean    q3   max
##   <chr>    <dbl> <dbl>  <dbl> <dbl> <dbl> <dbl>
## 1 87398500  0.38 3.11    4.41  4.57  6.2   12.4
## 2 87398900  3.52 5.25    5.96  6.61  7.3   13.8
## 3 87398950  1.62 3.68    4.92  5.28  6.64  11.9
## 4 87398980  3.37 5.5     6.17  6.48  7.14  13.1
## 5 87405500  0.2  1.3     2.53  2.83  3.66   9.8
## 6 87406900  0.1  0.865   2.4   2.43  3.05   9.1
## 7 87409900  0.1  0.92    2.03  2.43  3.5    8.1

7.0.2 Demanda Bioquímica de Oxigênio

grid.arrange(dbo_p1, dbo_p2, dbo_p3, ncol = 3)

(sum_dbo_p1 <- plan_wide_19902020 %>%
   select(CODIGO, DBO, ANO_COLETA) %>% 
   filter(ANO_COLETA>"1990" &
            ANO_COLETA<="2000") %>% 
   group_by(CODIGO) %>% 
   summarize(
     min = 
       min(DBO, 
           na.rm = TRUE),
     q1 = 
       quantile(DBO, 0.25, 
                na.rm = TRUE),
     median = 
       median(DBO, 
              na.rm = TRUE),
     mean = 
       mean(DBO, 
            na.rm= TRUE),
     q3 = 
       quantile(DBO, 0.75, 
                na.rm = TRUE),
     max = 
       max(DBO, 
           na.rm = TRUE))
)
## # A tibble: 7 x 7
##   CODIGO     min    q1 median  mean    q3   max
##   <chr>    <dbl> <dbl>  <dbl> <dbl> <dbl> <dbl>
## 1 87398500     1     1      2  1.86   2      13
## 2 87398900     1     1      1  1.52   2       6
## 3 87398950     1     1      1  1.66   2       6
## 4 87398980     1     1      1  1.13   1       2
## 5 87405500     1     2      3  5.37   5      64
## 6 87406900     1     4      5  9     11      26
## 7 87409900     2     3      4  6.97   9.5    31
(sum_dbo_p2 <- plan_wide_19902020 %>%
    select(CODIGO, DBO, ANO_COLETA) %>% 
    filter(ANO_COLETA>"2000" &
             ANO_COLETA<="2010") %>% 
    group_by(CODIGO) %>% 
    summarize(
      min = 
        min(DBO, 
            na.rm = TRUE),
      q1 = 
        quantile(DBO, 0.25, 
                 na.rm = TRUE),
      median = 
        median(DBO, 
               na.rm = TRUE),
      mean = 
        mean(DBO, 
             na.rm= TRUE),
      q3 = 
        quantile(DBO, 0.75, 
                 na.rm = TRUE),
      max = 
        max(DBO, 
            na.rm = TRUE))
)
## # A tibble: 7 x 7
##   CODIGO     min    q1 median  mean    q3   max
##   <chr>    <dbl> <dbl>  <dbl> <dbl> <dbl> <dbl>
## 1 87398500     1     1      1  1.58   2       5
## 2 87398900     1     1      1  1.40   2       5
## 3 87398950     1     1      1  1.66   2       5
## 4 87398980     1     1      1  1.30   1       5
## 5 87405500     1     2      4  4.67   6.5    14
## 6 87406900     1     3      5  6.53   8      28
## 7 87409900     1     3      6  6.31   9      15
(sum_dbo_p3 <- plan_wide_19902020 %>%
    select(CODIGO, DBO, ANO_COLETA) %>% 
    filter(ANO_COLETA>"2010" &
             ANO_COLETA<="2020") %>% 
    group_by(CODIGO) %>% 
    summarize(
      min = 
        min(DBO, 
            na.rm = TRUE),
      q1 = 
        quantile(DBO, 0.25, 
                 na.rm = TRUE),
      median = 
        median(DBO, 
               na.rm = TRUE),
      mean = 
        mean(DBO, 
             na.rm= TRUE),
      q3 = 
        quantile(DBO, 0.75, 
                 na.rm = TRUE),
      max = 
        max(DBO, 
            na.rm = TRUE))
)
## # A tibble: 7 x 7
##   CODIGO     min    q1 median  mean    q3   max
##   <chr>    <dbl> <dbl>  <dbl> <dbl> <dbl> <dbl>
## 1 87398500     1     1    1.5  2.15  3        7
## 2 87398900     1     1    1    1.51  2        5
## 3 87398950     1     1    2    2.65  2       18
## 4 87398980     1     1    1    1.32  2        2
## 5 87405500     1     3    4    5.28  6.25    21
## 6 87406900     1     3    5    6.58 10       24
## 7 87409900     1     3    4.5  6.18  8       18
ggsave("dbo_p1.png",
       plot = dbo_p1,
       path = "./graficos",
       dpi = 300,
       type = "cairo")
## Saving 10 x 6.66 in image
## Warning: Using ragg device as default. Ignoring `type` and `antialias` arguments
## Warning: Transformation introduced infinite values in continuous y-axis
## Warning: Removed 22 rows containing non-finite values (stat_boxplot).
## Removed 22 rows containing non-finite values (stat_boxplot).
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 22 rows containing missing values (position_quasirandom).
ggsave("dbo_p2.png",
       plot = dbo_p2,
       path = "./graficos",
       dpi = 300,
       type = "cairo")
## Saving 10 x 6.66 in image
## Warning: Using ragg device as default. Ignoring `type` and `antialias` arguments
## Warning: Transformation introduced infinite values in continuous y-axis
## Warning: Removed 30 rows containing non-finite values (stat_boxplot).
## Removed 30 rows containing non-finite values (stat_boxplot).
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 30 rows containing missing values (position_quasirandom).
ggsave("dbo_p3.png",
       plot = dbo_p3,
       path = "./graficos",
       dpi = 300,
       type = "cairo")
## Saving 10 x 6.66 in image
## Warning: Using ragg device as default. Ignoring `type` and `antialias` arguments
## Warning: Transformation introduced infinite values in continuous y-axis
## Warning: Removed 8 rows containing non-finite values (stat_boxplot).
## Removed 8 rows containing non-finite values (stat_boxplot).
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 8 rows containing missing values (position_quasirandom).
ggsave("dbo_3periodos.png",
       units = c("px"),
       width = 4500,
       height = 2993,
       plot = grid.arrange(dbo_p1, dbo_p2, dbo_p3, ncol = 3),
       path = "./graficos",
       dpi = 300,
       type = "cairo")
## Warning: Transformation introduced infinite values in continuous y-axis
## Warning: Removed 22 rows containing non-finite values (stat_boxplot).
## Removed 22 rows containing non-finite values (stat_boxplot).
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 22 rows containing missing values (position_quasirandom).
## Warning: Transformation introduced infinite values in continuous y-axis
## Warning: Removed 30 rows containing non-finite values (stat_boxplot).
## Removed 30 rows containing non-finite values (stat_boxplot).
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 30 rows containing missing values (position_quasirandom).
## Warning: Transformation introduced infinite values in continuous y-axis
## Warning: Removed 8 rows containing non-finite values (stat_boxplot).
## Removed 8 rows containing non-finite values (stat_boxplot).
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 8 rows containing missing values (position_quasirandom).
## Warning: Using ragg device as default. Ignoring `type` and `antialias` arguments

### Fósforo total

(ptot_p1<-ggplot(plan_wide_19902020%>% 
                   filter(ANO_COLETA>"1990" &
                            ANO_COLETA<="2000"),
                 aes(CODIGO,
                     `Fósforo total`))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=0.15,
            ymax=Inf,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=0.1,
            ymax=0.15,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=0,
            ymax=0.1,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "Fósforo total no período 1990-2000",
        x="Estação",
        y="mg/L")+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_y_continuous(expand = expansion(mult = c(0.03,0.03)),
                      n.breaks = 8,
                      limits = c(min(plan_wide_19902020$`Fósforo total`, na.rm = TRUE),
                                 max(plan_wide_19902020$`Fósforo total`), na.rm = TRUE),
                      trans = "log10")+
   scale_x_discrete(limits = c("87398500", "87398980", "87398900", 
                               "87398950", "87405500", "87406900", "87409900"))+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
 theme_grafs()
)

(ptot_p2 <- ggplot(plan_wide_19902020%>% 
                     filter(ANO_COLETA>"2000" &
                              ANO_COLETA<="2010"),
                   aes(CODIGO,
                       `Fósforo total`))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=0.15,
            ymax=Inf,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=0.1,
            ymax=0.15,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=0,
            ymax=0.1,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "Fósforo total no período 2000-2010",
        x="Estação",
        y="mg/L")+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_y_continuous(expand = expansion(mult = c(0.03,0.03)),
                      n.breaks = 8,
                      limits = c(min(plan_wide_19902020$`Fósforo total`, na.rm = TRUE),
                                 max(plan_wide_19902020$`Fósforo total`), na.rm = TRUE),
                      trans = "log10")+
   scale_x_discrete(limits = c("87398500", "87398980", "87398900", 
                               "87398950", "87405500", "87406900", "87409900"))+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
 theme_grafs()
)

(ptot_p3 <- ggplot(plan_wide_19902020%>% 
                     filter(ANO_COLETA>"2010" &
                              ANO_COLETA<="2020"),
                   aes(CODIGO,
                       `Fósforo total`))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=0.15,
            ymax=Inf,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=0.1,
            ymax=0.15,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=0,
            ymax=0.1,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "Fósforo total no período 2010-2020",
        x="Estação",
        y="mg/L")+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_y_continuous(expand = expansion(mult = c(0.03,0.03)),
                      n.breaks = 8,
                      limits = c(min(plan_wide_19902020$`Fósforo total`, na.rm = TRUE),
                                 max(plan_wide_19902020$`Fósforo total`), na.rm = TRUE),
                      trans = "log10")+
   scale_x_discrete(limits = c("87398500", "87398980", "87398900", 
                               "87398950", "87405500", "87406900", "87409900"))+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
)

grid.arrange(ptot_p1, ptot_p2, ptot_p3, ncol = 3)

(sum_ptot_p1 <- plan_wide_19902020 %>%
   select(CODIGO, `Fósforo total`, ANO_COLETA) %>% 
   filter(ANO_COLETA>"1990" &
            ANO_COLETA<="2000") %>% 
   group_by(CODIGO) %>% 
   summarize(
     min = 
       min(`Fósforo total`, na.rm = TRUE),
     q1 = 
       quantile(`Fósforo total`, 0.25, na.rm = TRUE),
     median = 
       median(`Fósforo total`, na.rm = TRUE),
     mean = 
       mean(`Fósforo total`, na.rm= TRUE),
     q3 = 
       quantile(`Fósforo total`, 0.75, na.rm = TRUE),
     max = 
       max(`Fósforo total`, na.rm = TRUE)))
## # A tibble: 7 x 7
##   CODIGO      min     q1 median   mean     q3   max
##   <chr>     <dbl>  <dbl>  <dbl>  <dbl>  <dbl> <dbl>
## 1 87398500 0.0097 0.0593 0.0881 0.123  0.14   0.863
## 2 87398900 0.0023 0.0468 0.0678 0.0747 0.0883 0.247
## 3 87398950 0.0202 0.0544 0.0737 0.0751 0.0904 0.179
## 4 87398980 0.01   0.0254 0.0547 0.0708 0.114  0.189
## 5 87405500 0.017  0.171  0.281  0.417  0.492  2.32 
## 6 87406900 0.156  0.270  0.508  0.785  1.07   2.79 
## 7 87409900 0.107  0.258  0.384  0.489  0.712  1.53
(sum_ptot_p2 <- plan_wide_19902020 %>%
    select(CODIGO, `Fósforo total`, ANO_COLETA) %>% 
    filter(ANO_COLETA>"2000" &
             ANO_COLETA<="2010") %>% 
    group_by(CODIGO) %>% 
    summarize(
      min = 
        min(`Fósforo total`, na.rm = TRUE),
      q1 = 
        quantile(`Fósforo total`, 0.25, na.rm = TRUE),
      median = 
        median(`Fósforo total`, na.rm = TRUE),
      mean = 
        mean(`Fósforo total`, na.rm= TRUE),
      q3 = 
        quantile(`Fósforo total`, 0.75, na.rm = TRUE),
      max = 
        max(`Fósforo total`, na.rm = TRUE)))
## # A tibble: 7 x 7
##   CODIGO      min     q1 median  mean    q3   max
##   <chr>     <dbl>  <dbl>  <dbl> <dbl> <dbl> <dbl>
## 1 87398500 0.025  0.094   0.131 0.148 0.16  0.637
## 2 87398900 0.015  0.0764  0.104 0.140 0.164 0.646
## 3 87398950 0.036  0.116   0.171 0.180 0.207 0.485
## 4 87398980 0.0115 0.052   0.076 0.101 0.103 1    
## 5 87405500 0.046  0.261   0.406 0.547 0.681 1.98 
## 6 87406900 0.056  0.338   0.599 0.752 0.967 3.49 
## 7 87409900 0.043  0.325   0.624 0.677 0.989 1.57
(sum_ptot_p3 <- plan_wide_19902020 %>%
    select(CODIGO, `Fósforo total`, ANO_COLETA) %>% 
    filter(ANO_COLETA>"2010" &
             ANO_COLETA<="2020") %>% 
    group_by(CODIGO) %>% 
    summarize(
      min = 
        min(`Fósforo total`, na.rm = TRUE),
      q1 = 
        quantile(`Fósforo total`, 0.25, na.rm = TRUE),
      median = 
        median(`Fósforo total`, na.rm = TRUE),
      mean = 
        mean(`Fósforo total`, na.rm= TRUE),
      q3 = 
        quantile(`Fósforo total`, 0.75, na.rm = TRUE),
      max = 
        max(`Fósforo total`, na.rm = TRUE)))
## # A tibble: 7 x 7
##   CODIGO     min     q1 median  mean    q3   max
##   <chr>    <dbl>  <dbl>  <dbl> <dbl> <dbl> <dbl>
## 1 87398500 0.061 0.118   0.163 0.166 0.186 0.381
## 2 87398900 0.057 0.0935  0.130 0.163 0.168 0.444
## 3 87398950 0.07  0.132   0.156 0.292 0.221 3.11 
## 4 87398980 0.019 0.0625  0.106 0.144 0.170 0.59 
## 5 87405500 0.013 0.187   0.332 0.361 0.45  0.803
## 6 87406900 0.089 0.254   0.364 0.448 0.560 1.26 
## 7 87409900 0.203 0.259   0.369 0.488 0.564 1.7
ggsave("ptot_p1.png",
       plot = ptot_p1,
       path = "./graficos",
       dpi = 300,
       type = "cairo")
## Saving 10 x 6.66 in image
## Warning: Using ragg device as default. Ignoring `type` and `antialias` arguments
## Warning: Transformation introduced infinite values in continuous y-axis
## Warning: Removed 47 rows containing non-finite values (stat_boxplot).
## Removed 47 rows containing non-finite values (stat_boxplot).
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 47 rows containing missing values (position_quasirandom).
ggsave("ptot_p2.png",
       plot = ptot_p2,
       path = "./graficos",
       dpi = 300,
       type = "cairo")
## Saving 10 x 6.66 in image
## Warning: Using ragg device as default. Ignoring `type` and `antialias` arguments
## Warning: Transformation introduced infinite values in continuous y-axis
## Warning: Removed 31 rows containing non-finite values (stat_boxplot).
## Removed 31 rows containing non-finite values (stat_boxplot).
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 31 rows containing missing values (position_quasirandom).
ggsave("ptot_p3.png",
       plot = ptot_p3,
       path = "./graficos",
       dpi = 300,
       type = "cairo")
## Saving 10 x 6.66 in image
## Warning: Using ragg device as default. Ignoring `type` and `antialias` arguments
## Warning: Transformation introduced infinite values in continuous y-axis
## Warning: Removed 54 rows containing non-finite values (stat_boxplot).
## Removed 54 rows containing non-finite values (stat_boxplot).
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 54 rows containing missing values (position_quasirandom).
ggsave("ptot_3periodos.png",
       units = c("px"),
       width = 4500,
       height = 2993,
       plot = grid.arrange(ptot_p1, ptot_p2, ptot_p3, ncol = 3),
       path = "./graficos",
       dpi = 300,
       type = "cairo")
## Warning: Transformation introduced infinite values in continuous y-axis
## Warning: Removed 47 rows containing non-finite values (stat_boxplot).
## Removed 47 rows containing non-finite values (stat_boxplot).
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 47 rows containing missing values (position_quasirandom).
## Warning: Transformation introduced infinite values in continuous y-axis
## Warning: Removed 31 rows containing non-finite values (stat_boxplot).
## Removed 31 rows containing non-finite values (stat_boxplot).
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 31 rows containing missing values (position_quasirandom).
## Warning: Transformation introduced infinite values in continuous y-axis
## Warning: Removed 54 rows containing non-finite values (stat_boxplot).
## Removed 54 rows containing non-finite values (stat_boxplot).
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 54 rows containing missing values (position_quasirandom).
## Warning: Using ragg device as default. Ignoring `type` and `antialias` arguments

7.0.3 Escherichia coli

(ecoli_p1 <- ggplot(plan_wide_19902020 %>% 
                      filter(ANO_COLETA>"1990" &
                               ANO_COLETA<="2000"),
                    aes(CODIGO,
                        `Escherichia coli`))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=3200,
            ymax=Inf,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=800,
            ymax=3200,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=160,
            ymax=800,
            alpha=1,
            fill="#70c18c")+ #classe 2
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=0,
            ymax=160,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "Escherichia coli no período 1990-2000",
        x="Estação",
        y="NMP/100mL")+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_y_continuous(expand = expansion(mult = c(0.01, 0.01)),
                      n.breaks = 9,
                      limits = c(min(plan_wide_19902020$`Escherichia coli`, na.rm = TRUE),
                                 max(plan_wide_19902020$`Escherichia coli`, na.rm = TRUE)),
                      trans = "log10",
                      labels = scales::number_format(accuracy = 1,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
   scale_x_discrete(limits = c("87398500", "87398980", "87398900", 
                               "87398950", "87405500", "87406900", "87409900"))+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
)

(ecoli_p2 <- ggplot(plan_wide_19902020 %>% 
                      filter(ANO_COLETA>"2000" &
                               ANO_COLETA<="2010"),
                    aes(CODIGO,
                        `Escherichia coli`))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=3200,
            ymax=Inf,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=800,
            ymax=3200,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=160,
            ymax=800,
            alpha=1,
            fill="#70c18c")+ #classe 2
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=0,
            ymax=160,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "Escherichia coli no período 2000-2010",
        x="Estação",
        y="NMP/100mL")+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_y_continuous(expand = expansion(mult = c(0.01, 0.01)),
                      n.breaks = 9,
                      limits = c(min(plan_wide_19902020$`Escherichia coli`, na.rm = TRUE),
                                 max(plan_wide_19902020$`Escherichia coli`, na.rm = TRUE)),
                      trans = "log10",
                      labels = scales::number_format(accuracy = 1,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
   scale_x_discrete(limits = c("87398500", "87398980", "87398900", 
                               "87398950", "87405500", "87406900", "87409900"))+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
)

(ecoli_p3 <- ggplot(plan_wide_19902020 %>% 
                      filter(ANO_COLETA>"2010" &
                               ANO_COLETA<="2020"),
                    aes(CODIGO,
                        `Escherichia coli`))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=3200,
            ymax=Inf,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=800,
            ymax=3200,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=160,
            ymax=800,
            alpha=1,
            fill="#70c18c")+ #classe 2
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=0,
            ymax=160,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "Escherichia coli no período 2010-2020",
        x="Estação",
        y="NMP/100mL")+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_y_continuous(expand = expansion(mult = c(0.01, 0.01)),
                      n.breaks = 9,
                      limits = c(min(plan_wide_19902020$`Escherichia coli`, na.rm = TRUE),
                                 max(plan_wide_19902020$`Escherichia coli`, na.rm = TRUE)),
                      trans = "log10",
                      labels = scales::number_format(accuracy = 1,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
   scale_x_discrete(limits = c("87398500", "87398980", "87398900", 
                               "87398950", "87405500", "87406900", "87409900"))+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
)

grid.arrange(ecoli_p1, ecoli_p2, ecoli_p3, ncol = 3)

(sum_ecoli_p1 <- plan_wide_19902020 %>%
   select(CODIGO, `Escherichia coli`, ANO_COLETA) %>% 
   filter(ANO_COLETA>"1990" &
            ANO_COLETA<="2000") %>% 
   group_by(CODIGO) %>% 
   summarize(
     min = 
       min(`Escherichia coli`, 
           na.rm = TRUE),
     q1 = 
       quantile(`Escherichia coli`, 0.25, 
                na.rm = TRUE),
     median = 
       median(`Escherichia coli`, 
              na.rm = TRUE),
     mean = 
       mean(`Escherichia coli`, 
            na.rm= TRUE),
     q3 = 
       quantile(`Escherichia coli`, 0.75, 
                na.rm = TRUE),
     max = 
       max(`Escherichia coli`, 
           na.rm = TRUE))
)
## # A tibble: 7 x 7
##   CODIGO     min    q1 median   mean    q3   max
##   <chr>    <dbl> <dbl>  <dbl>  <dbl> <dbl> <dbl>
## 1 87398500  32   136     240   854.    720 19200
## 2 87398900  16    68     160   548.    480  7760
## 3 87398950   2.4  12.8   268  4039.  10000 28000
## 4 87398980   4   160     243. 2907.    446 25600
## 5 87405500   1.6  12.8    24   545.    128 18400
## 6 87406900  13.6  61.6   192   718.    414 12800
## 7 87409900   2.4  12.8    64    97.7   128   720
(sum_ecoli_p2 <- plan_wide_19902020 %>%
    select(CODIGO, `Escherichia coli`, ANO_COLETA) %>% 
    filter(ANO_COLETA>"2000" &
             ANO_COLETA<="2010") %>% 
    group_by(CODIGO) %>% 
    summarize(
      min = 
        min(`Escherichia coli`, 
            na.rm = TRUE),
      q1 = 
        quantile(`Escherichia coli`, 0.25, 
                 na.rm = TRUE),
      median = 
        median(`Escherichia coli`, 
               na.rm = TRUE),
      mean = 
        mean(`Escherichia coli`, 
             na.rm= TRUE),
      q3 = 
        quantile(`Escherichia coli`, 0.75, 
                 na.rm = TRUE),
      max = 
        max(`Escherichia coli`, 
            na.rm = TRUE))
)
## # A tibble: 7 x 7
##   CODIGO     min    q1 median   mean     q3    max
##   <chr>    <dbl> <dbl>  <dbl>  <dbl>  <dbl>  <dbl>
## 1 87398500  21.6   91    150   1335.   308   27200
## 2 87398900  11     70    133.   444.   414.   2600
## 3 87398950  20    400    720    935.  1120    5500
## 4 87398980  24    110.   195    410.   289.   8800
## 5 87405500   4.7  162   2400  25445. 12950  490000
## 6 87406900   8    172  12800  66370. 62300  650000
## 7 87409900  16   7355. 35500  72440. 68750  460000
(sum_ecoli_p3 <- plan_wide_19902020 %>%
    select(CODIGO, `Escherichia coli`, ANO_COLETA) %>% 
    filter(ANO_COLETA>"2010" &
             ANO_COLETA<="2020") %>% 
    group_by(CODIGO) %>% 
    summarize(
      min = 
        min(`Escherichia coli`, 
            na.rm = TRUE),
      q1 = 
        quantile(`Escherichia coli`, 0.25, 
                 na.rm = TRUE),
      median = 
        median(`Escherichia coli`, 
               na.rm = TRUE),
      mean = 
        mean(`Escherichia coli`, 
             na.rm= TRUE),
      q3 = 
        quantile(`Escherichia coli`, 0.75, 
                 na.rm = TRUE),
      max = 
        max(`Escherichia coli`, 
            na.rm = TRUE))
)
## # A tibble: 7 x 7
##   CODIGO      min      q1 median    mean      q3      max
##   <chr>     <dbl>   <dbl>  <dbl>   <dbl>   <dbl>    <dbl>
## 1 87398500   90     155.    260     409.    451     2420 
## 2 87398900   10      52.8   107     245.    313     1553.
## 3 87398950  108.    250     487    1424.   1553.   10462 
## 4 87398980   40.8   140.    242.    529.    738.    2400 
## 5 87405500  632    8965   19232. 109992.  70750  1400000 
## 6 87406900 1440   23100   34500  230828. 140500  3400000 
## 7 87409900 2000   20100   38400   83128.  83680   345000
ggsave("ecoli_p1.png",
       plot = ecoli_p1,
       path = "./graficos",
       dpi = 300,
       type = "cairo")
## Saving 10 x 6.66 in image
## Warning: Using ragg device as default. Ignoring `type` and `antialias` arguments
## Warning: Transformation introduced infinite values in continuous y-axis
## Warning: Removed 15 rows containing non-finite values (stat_boxplot).
## Removed 15 rows containing non-finite values (stat_boxplot).
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 15 rows containing missing values (position_quasirandom).
ggsave("ecoli_p2.png",
       plot = ecoli_p2,
       path = "./graficos",
       dpi = 300,
       type = "cairo")
## Saving 10 x 6.66 in image
## Warning: Using ragg device as default. Ignoring `type` and `antialias` arguments
## Warning: Transformation introduced infinite values in continuous y-axis
## Warning: Removed 66 rows containing non-finite values (stat_boxplot).
## Removed 66 rows containing non-finite values (stat_boxplot).
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 66 rows containing missing values (position_quasirandom).
ggsave("ecoli_p3.png",
       plot = ecoli_p3,
       path = "./graficos",
       dpi = 300,
       type = "cairo")
## Saving 10 x 6.66 in image
## Warning: Using ragg device as default. Ignoring `type` and `antialias` arguments
## Warning: Transformation introduced infinite values in continuous y-axis
## Warning: Removed 14 rows containing non-finite values (stat_boxplot).
## Removed 14 rows containing non-finite values (stat_boxplot).
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 14 rows containing missing values (position_quasirandom).
ggsave("ecoli_3periodos.png",
       units = c("px"),
       width = 4500,
       height = 2993,
       plot = grid.arrange(ecoli_p1, ecoli_p2, ecoli_p3, ncol = 3),
       path = "./graficos",
       dpi = 300,
       type = "cairo")
## Warning: Transformation introduced infinite values in continuous y-axis
## Warning: Removed 15 rows containing non-finite values (stat_boxplot).
## Removed 15 rows containing non-finite values (stat_boxplot).
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 15 rows containing missing values (position_quasirandom).
## Warning: Transformation introduced infinite values in continuous y-axis
## Warning: Removed 66 rows containing non-finite values (stat_boxplot).
## Removed 66 rows containing non-finite values (stat_boxplot).
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 66 rows containing missing values (position_quasirandom).
## Warning: Transformation introduced infinite values in continuous y-axis
## Warning: Removed 14 rows containing non-finite values (stat_boxplot).
## Removed 14 rows containing non-finite values (stat_boxplot).
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 14 rows containing missing values (position_quasirandom).
## Warning: Using ragg device as default. Ignoring `type` and `antialias` arguments

### Nitrogênio amoniacal

(namon_p1 <- ggplot(plan_wide_19902020 %>% 
                      filter(ANO_COLETA>"1990" &
                               ANO_COLETA<="2000"),
                    aes(CODIGO,
                        `Nitrogênio total`))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=13.3,
            ymax=Inf,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=3.7,
            ymax=13.3,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=0,
            ymax=3.7,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "Nitrogênio amoniacal no período 1990-2000",
        x="Estação",
        y="mg/L")+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_y_continuous(expand = expansion(mult = c(0.01, 0.05)),
                      n.breaks = 9,
                      limits = c(min(plan_wide_19902020$`Nitrogênio total`, na.rm = TRUE),
                                 max(plan_wide_19902020$`Nitrogênio total`, na.rm = TRUE)),
                      trans = "log10",
                      labels = scales::number_format(accuracy = .001,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
   scale_x_discrete(limits = c("87398500", "87398980", "87398900", 
                               "87398950", "87405500", "87406900", "87409900"))+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
)

(namon_p2 <- ggplot(plan_wide_19902020 %>% 
                      filter(ANO_COLETA>"2000" &
                               ANO_COLETA<="2010"),
                    aes(CODIGO,
                        `Nitrogênio total`))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=13.3,
            ymax=Inf,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=3.7,
            ymax=13.3,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=0,
            ymax=3.7,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "Nitrogênio amoniacal no período 2000-2010",
        x="Estação",
        y="mg/L")+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_y_continuous(expand = expansion(mult = c(0.01, 0.05)),
                      n.breaks = 9,
                      limits = c(min(plan_wide_19902020$`Nitrogênio total`, na.rm = TRUE),
                                 max(plan_wide_19902020$`Nitrogênio total`, na.rm = TRUE)),
                      trans = "log10",
                      labels = scales::number_format(accuracy = .001,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
   scale_x_discrete(limits = c("87398500", "87398980", "87398900", 
                               "87398950", "87405500", "87406900", "87409900"))+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
)

(namon_p3 <- ggplot(plan_wide_19902020 %>% 
                      filter(ANO_COLETA>"2010" &
                               ANO_COLETA<="2020"),
                    aes(CODIGO,
                        `Nitrogênio total`))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=13.3,
            ymax=Inf,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=3.7,
            ymax=13.3,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=0,
            ymax=3.7,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "Nitrogênio amoniacal no período 2010-2020",
        x="Estação",
        y="mg/L")+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_y_continuous(expand = expansion(mult = c(0.01, 0.05)),
                      n.breaks = 9,
                      limits = c(min(plan_wide_19902020$`Nitrogênio total`, na.rm = TRUE),
                                 max(plan_wide_19902020$`Nitrogênio total`, na.rm = TRUE)),
                      trans = "log10",
                      labels = scales::number_format(accuracy = .001,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
   scale_x_discrete(limits = c("87398500", "87398980", "87398900", 
                               "87398950", "87405500", "87406900", "87409900"))+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
)

grid.arrange(namon_p1, namon_p2, namon_p3, ncol = 3)

(sum_namon_p1 <- plan_wide_19902020 %>%
   select(CODIGO, `Nitrogênio total`, ANO_COLETA) %>% 
   filter(ANO_COLETA>"1990" &
            ANO_COLETA<="2000") %>% 
   group_by(CODIGO) %>% 
   summarize(
     min = 
       min(`Nitrogênio total`, 
           na.rm = TRUE),
     q1 = 
       quantile(`Nitrogênio total`, 0.25, 
                na.rm = TRUE),
     median = 
       median(`Nitrogênio total`, 
              na.rm = TRUE),
     mean = 
       mean(`Nitrogênio total`, 
            na.rm= TRUE),
     q3 = 
       quantile(`Nitrogênio total`, 0.75, 
                na.rm = TRUE),
     max = 
       max(`Nitrogênio total`, 
           na.rm = TRUE))
)
## # A tibble: 7 x 7
##   CODIGO     min    q1 median  mean    q3   max
##   <chr>    <dbl> <dbl>  <dbl> <dbl> <dbl> <dbl>
## 1 87398500 0.44  0.842  1.00  1.22   1.34  3.81
## 2 87398900 0.22  0.82   1     1.09   1.25  4.86
## 3 87398950 0.51  0.83   1.02  1.06   1.19  2.16
## 4 87398980 0.549 0.68   0.755 0.872  1.01  1.85
## 5 87405500 0.51  1.53   2.94  5.27   6.77 21.6 
## 6 87406900 1.34  2.60   4.56  7.58  11.2  29.1 
## 7 87409900 0.5   1.98   4.29  5.18   7.01 19.6
(sum_namon_p2 <- plan_wide_19902020 %>%
    select(CODIGO, `Nitrogênio total`, ANO_COLETA) %>% 
    filter(ANO_COLETA>"2000" &
             ANO_COLETA<="2010") %>% 
    group_by(CODIGO) %>% 
    summarize(
      min = 
        min(`Nitrogênio total`, 
            na.rm = TRUE),
      q1 = 
        quantile(`Nitrogênio total`, 0.25, 
                 na.rm = TRUE),
      median = 
        median(`Nitrogênio total`, 
               na.rm = TRUE),
      mean = 
        mean(`Nitrogênio total`, 
             na.rm= TRUE),
      q3 = 
        quantile(`Nitrogênio total`, 0.75, 
                 na.rm = TRUE),
      max = 
        max(`Nitrogênio total`, 
            na.rm = TRUE))
)
## # A tibble: 7 x 7
##   CODIGO     min    q1 median  mean    q3   max
##   <chr>    <dbl> <dbl>  <dbl> <dbl> <dbl> <dbl>
## 1 87398500 0.18  0.885  0.992  1.80  1.46 23.2 
## 2 87398900 0.48  0.894  1.13   1.38  1.57  7.92
## 3 87398950 0.57  1.26   1.45   1.43  1.71  1.98
## 4 87398980 0.19  0.685  0.79   1.05  1.10  5.2 
## 5 87405500 0.968 2      3.29   5.45  6.60 21.7 
## 6 87406900 0.77  2.4    4.54   7.30 10.2  39.1 
## 7 87409900 1.62  2.5    6.97   7.92 10.6  21.5
(sum_namon_p3 <- plan_wide_19902020 %>%
    select(CODIGO, `Nitrogênio total`, ANO_COLETA) %>% 
    filter(ANO_COLETA>"2010" &
             ANO_COLETA<="2020") %>% 
    group_by(CODIGO) %>% 
    summarize(
      min = 
        min(`Nitrogênio total`, 
            na.rm = TRUE),
      q1 = 
        quantile(`Nitrogênio total`, 0.25, 
                 na.rm = TRUE),
      median = 
        median(`Nitrogênio total`, 
               na.rm = TRUE),
      mean = 
        mean(`Nitrogênio total`, 
             na.rm= TRUE),
      q3 = 
        quantile(`Nitrogênio total`, 0.75, 
                 na.rm = TRUE),
      max = 
        max(`Nitrogênio total`, 
            na.rm = TRUE))
)
## # A tibble: 7 x 7
##   CODIGO     min    q1 median  mean    q3   max
##   <chr>    <dbl> <dbl>  <dbl> <dbl> <dbl> <dbl>
## 1 87398500 0.222 0.89    1.11  1.24  1.41  2.56
## 2 87398900 0.095 0.883   1.02  1.29  1.40  4.25
## 3 87398950 0.612 1.04    1.43  1.90  2.06  9.5 
## 4 87398980 0.216 0.973   1.12  1.22  1.58  2.32
## 5 87405500 1.12  2.03    3.14  4.50  5.93 22.0 
## 6 87406900 1.37  2.40    5.58  6.47  7.58 25   
## 7 87409900 1.11  3       6.15  7.29  7.75 36
ggsave("namon_p1.png",
       plot = namon_p1,
       path = "./graficos",
       dpi = 300,
       type = "cairo")
## Saving 10 x 6.66 in image
## Warning: Using ragg device as default. Ignoring `type` and `antialias` arguments
## Warning: Transformation introduced infinite values in continuous y-axis
## Warning: Removed 102 rows containing non-finite values (stat_boxplot).
## Removed 102 rows containing non-finite values (stat_boxplot).
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 102 rows containing missing values (position_quasirandom).
ggsave("namon_p2.png",
       plot = namon_p2,
       path = "./graficos",
       dpi = 300,
       type = "cairo")
## Saving 10 x 6.66 in image
## Warning: Using ragg device as default. Ignoring `type` and `antialias` arguments
## Warning: Transformation introduced infinite values in continuous y-axis
## Warning: Removed 110 rows containing non-finite values (stat_boxplot).
## Removed 110 rows containing non-finite values (stat_boxplot).
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 110 rows containing missing values (position_quasirandom).
ggsave("namon_p3.png",
       plot = namon_p3,
       path = "./graficos",
       dpi = 300,
       type = "cairo")
## Saving 10 x 6.66 in image
## Warning: Using ragg device as default. Ignoring `type` and `antialias` arguments
## Warning: Transformation introduced infinite values in continuous y-axis
## Warning: Removed 70 rows containing non-finite values (stat_boxplot).
## Removed 70 rows containing non-finite values (stat_boxplot).
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 70 rows containing missing values (position_quasirandom).
ggsave("namon_3periodos.png",
       units = c("px"),
       width = 4500,
       height = 2993,
       plot = grid.arrange(namon_p1, namon_p2, namon_p3, ncol = 3),
       path = "./graficos",
       dpi = 300,
       type = "cairo")
## Warning: Transformation introduced infinite values in continuous y-axis
## Warning: Removed 102 rows containing non-finite values (stat_boxplot).
## Removed 102 rows containing non-finite values (stat_boxplot).
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 102 rows containing missing values (position_quasirandom).
## Warning: Transformation introduced infinite values in continuous y-axis
## Warning: Removed 110 rows containing non-finite values (stat_boxplot).
## Removed 110 rows containing non-finite values (stat_boxplot).
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 110 rows containing missing values (position_quasirandom).
## Warning: Transformation introduced infinite values in continuous y-axis
## Warning: Removed 70 rows containing non-finite values (stat_boxplot).
## Removed 70 rows containing non-finite values (stat_boxplot).
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 70 rows containing missing values (position_quasirandom).
## Warning: Using ragg device as default. Ignoring `type` and `antialias` arguments

7.0.4 Turbidez

(turb_p1 <- ggplot(plan_wide_19902020 %>% 
                     filter(ANO_COLETA>"1990" &
                              ANO_COLETA<="2000"),
                   aes(CODIGO,
                       Turbidez))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=100,
            ymax=Inf,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=40,
            ymax=100,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=0,
            ymax=40,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "Turbidez no período 1990-2000",
        x="Estação",
        y="UNT")+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_y_continuous(expand = expansion(mult = c(0.05, 0.03)),
                      n.breaks = 8,
                      limits = c(min(plan_wide_19902020$Turbidez, na.rm = TRUE),
                                 max(plan_wide_19902020$Turbidez, na.rm = TRUE)),
                      trans = "log10",
                      labels = scales::number_format(accuracy = 1,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
   scale_x_discrete(limits = c("87398500", "87398980", "87398900",
                               "87398950", "87405500", "87406900", "87409900"))+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
)

(turb_p2 <- ggplot(plan_wide_19902020 %>% 
                     filter(ANO_COLETA>"2000" &
                              ANO_COLETA<="2010"),
                   aes(CODIGO,
                       Turbidez))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=100,
            ymax=Inf,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=40,
            ymax=100,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=0,
            ymax=40,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "Turbidez no período 2000-2010",
        x="Estação",
        y="UNT")+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_y_continuous(expand = expansion(mult = c(0.05, 0.03)),
                      n.breaks = 8,
                      limits = c(min(plan_wide_19902020$Turbidez, na.rm = TRUE),
                                 max(plan_wide_19902020$Turbidez, na.rm = TRUE)),
                      trans = "log10",
                      labels = scales::number_format(accuracy = 1,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
   scale_x_discrete(limits = c("87398500", "87398980", "87398900", 
                               "87398950", "87405500", "87406900", "87409900"))+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
)

(turb_p3 <- ggplot(plan_wide_19902020 %>% 
                     filter(ANO_COLETA>"2010" &
                              ANO_COLETA<="2020"),
                   aes(CODIGO,
                       Turbidez))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=100,
            ymax=Inf,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=40,
            ymax=100,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=0,
            ymax=40,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "Turbidez no período 2010-2020",
        x="Estação",
        y="UNT")+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_y_continuous(expand = expansion(mult = c(0.05, 0.03)),
                      n.breaks = 8,
                      limits = c(min(plan_wide_19902020$Turbidez, na.rm = TRUE),
                                 max(plan_wide_19902020$Turbidez, na.rm = TRUE)),
                      trans = "log10",
                      labels = scales::number_format(accuracy = 1,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
   scale_x_discrete(limits = c("87398500", "87398980", "87398900", 
                               "87398950", "87405500", "87406900", "87409900"))+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
)

grid.arrange(turb_p1, turb_p2, turb_p3, ncol = 3)

(sum_turb_p1 <- plan_wide_19902020 %>%
   select(CODIGO, Turbidez, ANO_COLETA) %>% 
   filter(ANO_COLETA>"1990" &
            ANO_COLETA<="2000") %>% 
   group_by(CODIGO) %>% 
   summarize(
     min = 
       min(Turbidez, 
           na.rm = TRUE),
     q1 = 
       quantile(Turbidez, 0.25, 
                na.rm = TRUE),
     median = 
       median(Turbidez, 
              na.rm = TRUE),
     mean = 
       mean(Turbidez, 
            na.rm= TRUE),
     q3 = 
       quantile(Turbidez, 0.75, 
                na.rm = TRUE),
     max = 
       max(Turbidez, 
           na.rm = TRUE))
)
## # A tibble: 7 x 7
##   CODIGO     min    q1 median  mean    q3   max
##   <chr>    <dbl> <dbl>  <dbl> <dbl> <dbl> <dbl>
## 1 87398500   6.2  19     34.5  63.5  67     461
## 2 87398900   9    19     49.5  61.5  73.8   460
## 3 87398950   9.6  16     22    33.3  48.8   144
## 4 87398980  16    32.8   43    66.8  90.5   190
## 5 87405500   8.5  23.5   47    47.5  58     159
## 6 87406900  33    54.8   67    77.7  81.5   199
## 7 87409900   5.8  15     25    32.2  48      76
(sum_turb_p2 <- plan_wide_19902020 %>%
    select(CODIGO, Turbidez, ANO_COLETA) %>% 
    filter(ANO_COLETA>"2000" &
             ANO_COLETA<="2010") %>% 
    group_by(CODIGO) %>% 
    summarize(
      min = 
        min(Turbidez, 
            na.rm = TRUE),
      q1 = 
        quantile(Turbidez, 0.25, 
                 na.rm = TRUE),
      median = 
        median(Turbidez, 
               na.rm = TRUE),
      mean = 
        mean(Turbidez, 
             na.rm= TRUE),
      q3 = 
        quantile(Turbidez, 0.75, 
                 na.rm = TRUE),
      max = 
        max(Turbidez, 
            na.rm = TRUE))
)
## # A tibble: 7 x 7
##   CODIGO     min    q1 median  mean    q3   max
##   <chr>    <dbl> <dbl>  <dbl> <dbl> <dbl> <dbl>
## 1 87398500     9  41.2   55.5  71.1  74.2   428
## 2 87398900    39  57     78   107.  116.    475
## 3 87398950    39  47     64    96.5  90     330
## 4 87398980    24  37     50    64.5  87     176
## 5 87405500    32  46     63.5  70.3  76     341
## 6 87406900    35  49     62    69.9  75.5   284
## 7 87409900    40  45     60    70.4  90     151
(sum_turb_p3 <- plan_wide_19902020 %>%
    select(CODIGO, Turbidez, ANO_COLETA) %>% 
    filter(ANO_COLETA>"2010" &
             ANO_COLETA<="2020") %>% 
    group_by(CODIGO) %>% 
    summarize(
      min = 
        min(Turbidez, 
            na.rm = TRUE),
      q1 = 
        quantile(Turbidez, 0.25, 
                 na.rm = TRUE),
      median = 
        median(Turbidez, 
               na.rm = TRUE),
      mean = 
        mean(Turbidez, 
             na.rm= TRUE),
      q3 = 
        quantile(Turbidez, 0.75, 
                 na.rm = TRUE),
      max = 
        max(Turbidez, 
            na.rm = TRUE))
) 
## # A tibble: 7 x 7
##   CODIGO     min    q1 median  mean    q3   max
##   <chr>    <dbl> <dbl>  <dbl> <dbl> <dbl> <dbl>
## 1 87398500  8.52  16.4   29    33.3  43     85 
## 2 87398900 14.8   39.2   48.3  66.7  73.4  299 
## 3 87398950 16     29.9   41    51.6  65    230 
## 4 87398980 11     19.4   33.6  39.5  42.2  110.
## 5 87405500 10.0   29.0   41    42.9  54.5  131 
## 6 87406900  9.62  23     39    41.2  52    122 
## 7 87409900  9.68  22.0   34.0  40.5  47    182.
ggsave("turb_p1.png",
       plot = turb_p1,
       path = "./graficos",
       dpi = 300,
       type = "cairo")
## Saving 10 x 6.66 in image
## Warning: Using ragg device as default. Ignoring `type` and `antialias` arguments
## Warning: Transformation introduced infinite values in continuous y-axis
## Warning: Removed 56 rows containing non-finite values (stat_boxplot).
## Removed 56 rows containing non-finite values (stat_boxplot).
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 56 rows containing missing values (position_quasirandom).
ggsave("turb_p2.png",
       plot = turb_p2,
       path = "./graficos",
       dpi = 300,
       type = "cairo")
## Saving 10 x 6.66 in image
## Warning: Using ragg device as default. Ignoring `type` and `antialias` arguments
## Warning: Transformation introduced infinite values in continuous y-axis
## Warning: Removed 74 rows containing non-finite values (stat_boxplot).
## Removed 74 rows containing non-finite values (stat_boxplot).
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 74 rows containing missing values (position_quasirandom).
ggsave("turb_p3.png",
       plot = turb_p3,
       path = "./graficos",
       dpi = 300,
       type = "cairo")
## Saving 10 x 6.66 in image
## Warning: Using ragg device as default. Ignoring `type` and `antialias` arguments
## Warning: Transformation introduced infinite values in continuous y-axis
## Warning: Removed 14 rows containing non-finite values (stat_boxplot).
## Removed 14 rows containing non-finite values (stat_boxplot).
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 14 rows containing missing values (position_quasirandom).
ggsave("turb_3periodos.png",
       units = c("px"),
       width = 4500,
       height = 2993,
       plot = grid.arrange(turb_p1, turb_p2, turb_p3, ncol = 3),
       path = "./graficos",
       dpi = 300,
       type = "cairo")
## Warning: Transformation introduced infinite values in continuous y-axis
## Warning: Removed 56 rows containing non-finite values (stat_boxplot).
## Removed 56 rows containing non-finite values (stat_boxplot).
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 56 rows containing missing values (position_quasirandom).
## Warning: Transformation introduced infinite values in continuous y-axis
## Warning: Removed 74 rows containing non-finite values (stat_boxplot).
## Removed 74 rows containing non-finite values (stat_boxplot).
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 74 rows containing missing values (position_quasirandom).
## Warning: Transformation introduced infinite values in continuous y-axis
## Warning: Removed 14 rows containing non-finite values (stat_boxplot).
## Removed 14 rows containing non-finite values (stat_boxplot).
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 14 rows containing missing values (position_quasirandom).
## Warning: Using ragg device as default. Ignoring `type` and `antialias` arguments

7.0.5 pH

(pH_p1 <- ggplot(plan_wide_19902020 %>% 
                   filter(ANO_COLETA>"1990" &
                            ANO_COLETA<="2000"),
                 aes(CODIGO,
                     pH))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=-Inf,
            ymax=6,
            alpha=1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=9,
            ymax=Inf,
            alpha=1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=6,
            ymax=9,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "pH no período 1990-2000",
        x="Estação",
        y="")+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_y_continuous(expand = expansion(mult = c(0.01, 0.01)),
                      n.breaks = 8,
                      limits = c(4,11),
                      labels = scales::number_format(accuracy = 1,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
   scale_x_discrete(limits = c("87398500", "87398980", "87398900", 
                               "87398950", "87405500", "87406900", "87409900"))+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
)

(pH_p2 <- ggplot(plan_wide_19902020 %>% 
                   filter(ANO_COLETA>"2000" &
                            ANO_COLETA<="2010"),
                 aes(CODIGO,
                     pH))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=-Inf,
            ymax=6,
            alpha=1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=9,
            ymax=Inf,
            alpha=1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=6,
            ymax=9,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "pH no período 2000-2010",
        x="Estação",
        y="")+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_y_continuous(expand = expansion(mult = c(0.01, 0.01)),
                      n.breaks = 8,
                      limits = c(4,11),
                      labels = scales::number_format(accuracy = 1,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
   scale_x_discrete(limits = c("87398500", "87398980", "87398900",
                               "87398950", "87405500", "87406900", "87409900"))+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
)

(pH_p3 <- ggplot(plan_wide_19902020 %>% 
                   filter(ANO_COLETA>"2010" &
                            ANO_COLETA<="2020"),
                 aes(CODIGO,
                     pH))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=-Inf,
            ymax=6,
            alpha=1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=9,
            ymax=Inf,
            alpha=1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=6,
            ymax=9,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "pH no período 2010-2020",
        x="Estação",
        y="")+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_y_continuous(expand = expansion(mult = c(0.01, 0.01)),
                      n.breaks = 8,
                      limits = c(4,11),
                      labels = scales::number_format(accuracy = 1,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
   scale_x_discrete(limits = c("87398500", "87398980", "87398900",
                               "87398950", "87405500", "87406900", "87409900"))+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
)

grid.arrange(pH_p1, pH_p2, pH_p3, ncol = 3)

(sum_pH_p1 <- plan_wide_19902020 %>%
   select(CODIGO, pH, ANO_COLETA) %>% 
   filter(ANO_COLETA>"1990" &
            ANO_COLETA<="2000") %>% 
   group_by(CODIGO) %>% 
   summarize(
     min = 
       min(pH, 
           na.rm = TRUE),
     q1 = 
       quantile(pH, 0.25, 
                na.rm = TRUE),
     median = 
       median(pH, 
              na.rm = TRUE),
     mean = 
       mean(pH, 
            na.rm= TRUE),
     q3 = 
       quantile(pH, 0.75, 
                na.rm = TRUE),
     max = 
       max(pH, 
           na.rm = TRUE))
)
## # A tibble: 7 x 7
##   CODIGO     min    q1 median  mean    q3   max
##   <chr>    <dbl> <dbl>  <dbl> <dbl> <dbl> <dbl>
## 1 87398500   5    6.18   6.59  6.51  6.82   7.9
## 2 87398900   5.2  6      6.3   6.33  6.63   7.9
## 3 87398950   5.4  6.29   6.4   6.49  6.72   8.1
## 4 87398980   5.3  5.93   6.2   6.16  6.3    7.3
## 5 87405500   5    6.3    6.4   6.47  6.7    9.3
## 6 87406900   5.5  6.18   6.45  6.43  6.8    7.3
## 7 87409900   4.5  6.2    6.4   6.44  6.7    7.4
(sum_pH_p2 <- plan_wide_19902020 %>%
    select(CODIGO, pH, ANO_COLETA) %>% 
    filter(ANO_COLETA>"2000" &
             ANO_COLETA<="2010") %>% 
    group_by(CODIGO) %>% 
    summarize(
      min = 
        min(pH, 
            na.rm = TRUE),
      q1 = 
        quantile(pH, 0.25, 
                 na.rm = TRUE),
      median = 
        median(pH, 
               na.rm = TRUE),
      mean = 
        mean(pH, 
             na.rm= TRUE),
      q3 = 
        quantile(pH, 0.75, 
                 na.rm = TRUE),
      max = 
        max(pH, 
            na.rm = TRUE))
) 
## # A tibble: 7 x 7
##   CODIGO     min    q1 median  mean    q3   max
##   <chr>    <dbl> <dbl>  <dbl> <dbl> <dbl> <dbl>
## 1 87398500   5.3   6.3   6.6   6.59  6.88   7.9
## 2 87398900   5.5   6.4   6.65  6.63  6.9    7.5
## 3 87398950   6     6.6   6.8   6.89  7.25   7.6
## 4 87398980   5.8   6.3   6.5   6.63  7      7.5
## 5 87405500   5.2   6.4   6.6   6.68  6.9    8.3
## 6 87406900   5.5   6.4   6.7   6.66  6.9    8.6
## 7 87409900   5.8   6.5   6.8   6.77  7      8.4
(sum_pH_p3 <- plan_wide_19902020 %>%
    select(CODIGO, pH, ANO_COLETA) %>% 
    filter(ANO_COLETA>"2010" &
             ANO_COLETA<="2020") %>% 
    group_by(CODIGO) %>% 
    summarize(
      min = 
        min(pH, 
            na.rm = TRUE),
      q1 = 
        quantile(pH, 0.25, 
                 na.rm = TRUE),
      median = 
        median(pH, 
               na.rm = TRUE),
      mean = 
        mean(pH, 
             na.rm= TRUE),
      q3 = 
        quantile(pH, 0.75, 
                 na.rm = TRUE),
      max = 
        max(pH, 
            na.rm = TRUE))
)
## # A tibble: 7 x 7
##   CODIGO     min    q1 median  mean    q3   max
##   <chr>    <dbl> <dbl>  <dbl> <dbl> <dbl> <dbl>
## 1 87398500  5.47  6.28   6.42  6.47  6.60  7.3 
## 2 87398900  5.68  6.36   6.5   6.57  6.84  7.4 
## 3 87398950  5.71  6.28   6.46  6.46  6.68  7   
## 4 87398980  5.42  6.10   6.36  6.39  6.6   7.2 
## 5 87405500  5.64  6.34   6.5   6.49  6.7   7.01
## 6 87406900  5.6   6.4    6.48  6.51  6.77  7.3 
## 7 87409900  5.59  6.46   6.6   6.57  6.76  7.2
ggsave("pH_p1.png",
       plot = pH_p1,
       path = "./graficos",
       dpi = 300,
       type = "cairo")
## Saving 10 x 6.66 in image
## Warning: Using ragg device as default. Ignoring `type` and `antialias` arguments
## Warning: Removed 1 rows containing non-finite values (stat_boxplot).
## Removed 1 rows containing non-finite values (stat_boxplot).
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 1 rows containing missing values (position_quasirandom).
ggsave("pH_p2.png",
       plot = pH_p2,
       path = "./graficos",
       dpi = 300,
       type = "cairo")
## Saving 10 x 6.66 in image
## Warning: Using ragg device as default. Ignoring `type` and `antialias` arguments
## Warning: Removed 73 rows containing non-finite values (stat_boxplot).
## Removed 73 rows containing non-finite values (stat_boxplot).
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 73 rows containing missing values (position_quasirandom).
ggsave("pH_p3.png",
       plot = pH_p3,
       path = "./graficos",
       dpi = 300,
       type = "cairo")
## Saving 10 x 6.66 in image
## Warning: Using ragg device as default. Ignoring `type` and `antialias` arguments
## Warning: Removed 14 rows containing non-finite values (stat_boxplot).
## Removed 14 rows containing non-finite values (stat_boxplot).
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 14 rows containing missing values (position_quasirandom).
ggsave("pH_3periodos.png",
       units = c("px"),
       width = 4500,
       height = 2993,
       plot = grid.arrange(pH_p1, pH_p2, pH_p3, ncol = 3),
       path = "./graficos",
       dpi = 300,
       type = "cairo")
## Warning: Removed 1 rows containing non-finite values (stat_boxplot).
## Warning: Removed 1 rows containing non-finite values (stat_boxplot).
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 1 rows containing missing values (position_quasirandom).
## Warning: Removed 73 rows containing non-finite values (stat_boxplot).
## Removed 73 rows containing non-finite values (stat_boxplot).
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 73 rows containing missing values (position_quasirandom).
## Warning: Removed 14 rows containing non-finite values (stat_boxplot).
## Removed 14 rows containing non-finite values (stat_boxplot).
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 14 rows containing missing values (position_quasirandom).
## Warning: Using ragg device as default. Ignoring `type` and `antialias` arguments

7.0.6 Sólidos totais

(SolTot_p1 <- ggplot(plan_wide_19902020 %>% 
                       filter(ANO_COLETA>"1990" &
                                ANO_COLETA<="2000"),
                     aes(CODIGO,
                         `Sólidos totais`))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=500,
            ymax=Inf,
            alpha=1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=-Inf,
            ymax=500,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "Sólidos totais no período 1990-2000",
        x="Estação",
        y="")+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_y_continuous(expand = expansion(mult = c(0.01, 0.05)),
                      n.breaks = 8,
                      limits = c(0,
                                 max(plan_wide_19902020$`Sólidos totais`, na.rm = TRUE)),
                      labels = scales::number_format(accuracy = 1,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
   scale_x_discrete(limits = c("87398500", "87398980", "87398900", 
                               "87398950", "87405500", "87406900", "87409900"))+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
)

(SolTot_p2 <- ggplot(plan_wide_19902020 %>% 
                       filter(ANO_COLETA>"2000" &
                                ANO_COLETA<="2010"),
                     aes(CODIGO,
                         `Sólidos totais`))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=500,
            ymax=Inf,
            alpha=1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=-Inf,
            ymax=500,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "Sólidos totais no período 2000-2010",
        x="Estação",
        y="")+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_y_continuous(expand = expansion(mult = c(0.01, 0.05)),
                      n.breaks = 8,
                      limits = c(0,
                                 max(plan_wide_19902020$`Sólidos totais`, na.rm = TRUE)),
                      labels = scales::number_format(accuracy = 1,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
   scale_x_discrete(limits = c("87398500", "87398980", "87398900",
                               "87398950", "87405500", "87406900", "87409900"))+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
)

(SolTot_p3 <- ggplot(plan_wide_19902020 %>% 
                       filter(ANO_COLETA>"2010" &
                                ANO_COLETA<="2020"),
                     aes(CODIGO,
                         `Sólidos totais`))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=500,
            ymax=Inf,
            alpha=1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=-Inf,
            ymax=500,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "Sólidos totais no período 2010-2020",
        x="Estação",
        y="")+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_y_continuous(expand = expansion(mult = c(0.01, 0.05)),
                      n.breaks = 8,
                      limits = c(0,
                                 max(plan_wide_19902020$`Sólidos totais`, na.rm = TRUE)),
                      labels = scales::number_format(accuracy = 1,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
   scale_x_discrete(limits = c("87398500", "87398980", "87398900",
                               "87398950", "87405500", "87406900", "87409900"))+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
)

grid.arrange(SolTot_p1, SolTot_p2, SolTot_p3, ncol = 3)

(sum_SolTot_p1 <- plan_wide_19902020 %>%
   select(CODIGO, `Sólidos totais`, ANO_COLETA) %>% 
   filter(ANO_COLETA>"1990" &
            ANO_COLETA<="2000") %>% 
   group_by(CODIGO) %>% 
   summarize(
     min = 
       min(`Sólidos totais`, 
           na.rm = TRUE),
     q1 = 
       quantile(`Sólidos totais`, 0.25, 
                na.rm = TRUE),
     median = 
       median(`Sólidos totais`, 
              na.rm = TRUE),
     mean = 
       mean(`Sólidos totais`, 
            na.rm= TRUE),
     q3 = 
       quantile(`Sólidos totais`, 0.75, 
                na.rm = TRUE),
     max = 
       max(`Sólidos totais`, 
           na.rm = TRUE))
)
## # A tibble: 7 x 7
##   CODIGO     min    q1 median  mean    q3   max
##   <chr>    <dbl> <dbl>  <dbl> <dbl> <dbl> <dbl>
## 1 87398500    46  84.5   95   122.   120    510
## 2 87398900    18  74.5   97   111.   122.   474
## 3 87398950    10  76.5   91    90.9  106.   155
## 4 87398980    48  63.5   81.5 104.   126.   337
## 5 87405500    70 101    121   133.   151    361
## 6 87406900    89 118    155   165.   210    279
## 7 87409900    20  99.5  122   128.   143    381
(sum_SolTot_p2 <- plan_wide_19902020 %>%
    select(CODIGO, `Sólidos totais`, ANO_COLETA) %>% 
    filter(ANO_COLETA>"2000" &
             ANO_COLETA<="2010") %>% 
    group_by(CODIGO) %>% 
    summarize(
      min = 
        min(`Sólidos totais`, 
            na.rm = TRUE),
      q1 = 
        quantile(`Sólidos totais`, 0.25, 
                 na.rm = TRUE),
      median = 
        median(`Sólidos totais`, 
               na.rm = TRUE),
      mean = 
        mean(`Sólidos totais`, 
             na.rm= TRUE),
      q3 = 
        quantile(`Sólidos totais`, 0.75, 
                 na.rm = TRUE),
      max = 
        max(`Sólidos totais`, 
            na.rm = TRUE))
)
## # A tibble: 7 x 7
##   CODIGO     min    q1 median  mean    q3   max
##   <chr>    <dbl> <dbl>  <dbl> <dbl> <dbl> <dbl>
## 1 87398500    28  80     100  111.   123.   412
## 2 87398900    42  82     102. 128.   140.   489
## 3 87398950    46  94.2   108. 126.   127.   318
## 4 87398980    40  61      77   85.3   96    228
## 5 87405500    48 102     133  148.   170.   522
## 6 87406900    50 109     134. 154.   170.   670
## 7 87409900    56 112.    156  167.   190.   599
(sum_SolTot_p3 <- plan_wide_19902020 %>%
    select(CODIGO, `Sólidos totais`, ANO_COLETA) %>% 
    filter(ANO_COLETA>"2010" &
             ANO_COLETA<="2020") %>% 
    group_by(CODIGO) %>% 
    summarize(
      min = 
        min(`Sólidos totais`, 
            na.rm = TRUE),
      q1 = 
        quantile(`Sólidos totais`, 0.25, 
                 na.rm = TRUE),
      median = 
        median(`Sólidos totais`, 
               na.rm = TRUE),
      mean = 
        mean(`Sólidos totais`, 
             na.rm= TRUE),
      q3 = 
        quantile(`Sólidos totais`, 0.75, 
                 na.rm = TRUE),
      max = 
        max(`Sólidos totais`, 
            na.rm = TRUE))
)
## # A tibble: 7 x 7
##   CODIGO     min    q1 median  mean    q3   max
##   <chr>    <dbl> <dbl>  <dbl> <dbl> <dbl> <dbl>
## 1 87398500    61  69      90   82.8   96    101
## 2 87398900    41  77     104  120.   127    308
## 3 87398950    45  93     101  109.   117    221
## 4 87398980    55  62.8    80   79.9   95    109
## 5 87405500    83  89.2   108. 124.   162.   195
## 6 87406900    50 106     117  135.   163    246
## 7 87409900    75 103     115  131.   145    251
ggsave("SolTot_p1.png",
       plot = SolTot_p1,
       path = "./graficos",
       dpi = 300,
       type = "cairo")
## Saving 10 x 6.66 in image
## Warning: Using ragg device as default. Ignoring `type` and `antialias` arguments
## Warning: Removed 10 rows containing non-finite values (stat_boxplot).
## Removed 10 rows containing non-finite values (stat_boxplot).
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 10 rows containing missing values (position_quasirandom).
ggsave("SolTot_p2.png",
       plot = SolTot_p2,
       path = "./graficos",
       dpi = 300,
       type = "cairo")
## Saving 10 x 6.66 in image
## Warning: Using ragg device as default. Ignoring `type` and `antialias` arguments
## Warning: Removed 7 rows containing non-finite values (stat_boxplot).
## Removed 7 rows containing non-finite values (stat_boxplot).
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 7 rows containing missing values (position_quasirandom).
ggsave("SolTot_p3.png",
       plot = SolTot_p3,
       path = "./graficos",
       dpi = 300,
       type = "cairo")
## Saving 10 x 6.66 in image
## Warning: Using ragg device as default. Ignoring `type` and `antialias` arguments
## Warning: Removed 125 rows containing non-finite values (stat_boxplot).
## Removed 125 rows containing non-finite values (stat_boxplot).
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 125 rows containing missing values (position_quasirandom).
ggsave("SolTot_3periodos.png",
       units = c("px"),
       width = 4500,
       height = 2993,
       plot = grid.arrange(SolTot_p1, SolTot_p2, SolTot_p3, ncol = 3),
       path = "./graficos",
       dpi = 300,
       type = "cairo")
## Warning: Removed 10 rows containing non-finite values (stat_boxplot).
## Warning: Removed 10 rows containing non-finite values (stat_boxplot).
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 10 rows containing missing values (position_quasirandom).
## Warning: Removed 7 rows containing non-finite values (stat_boxplot).
## Removed 7 rows containing non-finite values (stat_boxplot).
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 7 rows containing missing values (position_quasirandom).
## Warning: Removed 125 rows containing non-finite values (stat_boxplot).
## Removed 125 rows containing non-finite values (stat_boxplot).
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 125 rows containing missing values (position_quasirandom).
## Warning: Using ragg device as default. Ignoring `type` and `antialias` arguments

7.0.7 IQA

grid.arrange(iqa_p1, iqa_p2, iqa_p3, ncol = 3)

(sum_IQA_p1 <- plan_wide_19902020 %>%
   select(CODIGO, IQA, ANO_COLETA) %>% 
   filter(ANO_COLETA>"1990" &
            ANO_COLETA<="2000") %>% 
   group_by(CODIGO) %>% 
   summarize(
     min = 
       min(IQA, 
           na.rm = TRUE),
     q1 = 
       quantile(IQA, 0.25, 
                na.rm = TRUE),
     median = 
       median(IQA, 
              na.rm = TRUE),
     mean = 
       mean(IQA, 
            na.rm= TRUE),
     q3 = 
       quantile(IQA, 0.75, 
                na.rm = TRUE),
     max = 
       max(IQA, 
           na.rm = TRUE))
)
## # A tibble: 7 x 7
##   CODIGO     min    q1 median  mean    q3   max
##   <chr>    <dbl> <dbl>  <dbl> <dbl> <dbl> <dbl>
## 1 87398500  27.0  35.7   40.9  40.7  46.2  52.2
## 2 87398900  27.8  37.9   42.9  43.0  48.0  58.5
## 3 87398950  32.8  36.8   41.4  43.2  48.6  61.9
## 4 87398980  29.2  35.8   40.4  40.3  44.8  51.9
## 5 87405500  24.8  34.9   41.2  40.3  46.9  57.6
## 6 87406900  24.7  31.3   37.8  37.4  44.4  49.0
## 7 87409900  23.6  31.9   37.1  38.8  46.2  55.4
(sum_IQA_p2 <- plan_wide_19902020 %>%
    select(CODIGO, IQA, ANO_COLETA) %>% 
    filter(ANO_COLETA>"2000" &
             ANO_COLETA<="2010") %>% 
    group_by(CODIGO) %>% 
    summarize(
      min = 
        min(IQA, 
            na.rm = TRUE),
      q1 = 
        quantile(IQA, 0.25, 
                 na.rm = TRUE),
      median = 
        median(IQA, 
               na.rm = TRUE),
      mean = 
        mean(IQA, 
             na.rm= TRUE),
      q3 = 
        quantile(IQA, 0.75, 
                 na.rm = TRUE),
      max = 
        max(IQA, 
            na.rm = TRUE))
)
## # A tibble: 7 x 7
##   CODIGO     min    q1 median  mean    q3   max
##   <chr>    <dbl> <dbl>  <dbl> <dbl> <dbl> <dbl>
## 1 87398500  27.8  34.6   40.0  39.5  43.5  48.7
## 2 87398900  28.5  35.1   37.6  38.3  40.6  48.5
## 3 87398950  21.1  29.4   32.7  32.8  36.8  44.0
## 4 87398980  24.5  35.7   39.4  39.5  43.4  52.1
## 5 87405500  19.8  28.7   31.5  31.9  35.7  48.8
## 6 87406900  17.1  25.3   29.0  29.5  32.8  44.1
## 7 87409900  16.2  20.5   26.1  25.0  29.8  33.1
(sum_IQA_p3 <- plan_wide_19902020 %>%
    select(CODIGO, IQA, ANO_COLETA) %>% 
    filter(ANO_COLETA>"2010" &
             ANO_COLETA<="2020") %>% 
    group_by(CODIGO) %>% 
    summarize(
      min = 
        min(IQA, 
            na.rm = TRUE),
      q1 = 
        quantile(IQA, 0.25, 
                 na.rm = TRUE),
      median = 
        median(IQA, 
               na.rm = TRUE),
      mean = 
        mean(IQA, 
             na.rm= TRUE),
      q3 = 
        quantile(IQA, 0.75, 
                 na.rm = TRUE),
      max = 
        max(IQA, 
            na.rm = TRUE),
      n = 
        length(IQA))
)
## # A tibble: 7 x 8
##   CODIGO     min    q1 median  mean    q3   max     n
##   <chr>    <dbl> <dbl>  <dbl> <dbl> <dbl> <dbl> <int>
## 1 87398500  40.2  42.5   45.4  44.2  45.5  47.2    34
## 2 87398900  34.1  38.6   41.2  40.2  42.9  44.4    36
## 3 87398950  36.7  39.5   42.4  41.5  44.4  44.6    35
## 4 87398980  40.0  40.0   40.0  40.0  40.0  40.0    28
## 5 87405500  30.8  31.6   32.5  32.5  33.3  34.1    33
## 6 87406900  22.9  24.4   25.9  25.3  26.5  27.2    35
## 7 87409900  24.1  25.1   27.3  26.9  28.2  29.7    37
plan_wide_19902020 %>% 
  select(CODIGO, IQA) %>% 
  group_by(CODIGO) %>% 
  summarize(
    min = 
      min(IQA, 
          na.rm = TRUE),
    q1 = 
      quantile(IQA, 0.25, 
               na.rm = TRUE),
    median = 
      median(IQA, 
             na.rm = TRUE),
    mean = 
      mean(IQA, 
           na.rm= TRUE),
    q3 = 
      quantile(IQA, 0.75, 
               na.rm = TRUE),
    max = 
      max(IQA, 
          na.rm = TRUE))
## # A tibble: 7 x 7
##   CODIGO     min    q1 median  mean    q3   max
##   <chr>    <dbl> <dbl>  <dbl> <dbl> <dbl> <dbl>
## 1 87398500  27.0  35.6   40.7  40.5  45.4  52.2
## 2 87398900  27.8  36.4   40.7  41.4  46.1  58.5
## 3 87398950  21.1  36.6   40.7  41.8  47.4  61.9
## 4 87398980  24.5  35.7   39.7  39.9  44.1  52.1
## 5 87405500  19.8  29.9   36.9  37.3  44.0  57.6
## 6 87406900  17.1  25.7   31.1  32.4  38.0  49.0
## 7 87409900  16.2  28.1   33.2  35.3  42.7  55.4
ggsave("iqa_p1.png",
       plot = iqa_p1,
       path = "./graficos",
       dpi = 300,
       type = "cairo")
## Saving 10 x 6.66 in image
## Warning: Using ragg device as default. Ignoring `type` and `antialias` arguments
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 164 rows containing missing values (position_quasirandom).
ggsave("iqa_p2.png",
       plot = iqa_p2,
       path = "./graficos",
       dpi = 300,
       type = "cairo")
## Saving 10 x 6.66 in image
## Warning: Using ragg device as default. Ignoring `type` and `antialias` arguments
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 253 rows containing missing values (position_quasirandom).
ggsave("iqa_p3.png",
       plot = iqa_p3,
       path = "./graficos",
       dpi = 300,
       type = "cairo")
## Saving 10 x 6.66 in image
## Warning: Using ragg device as default. Ignoring `type` and `antialias` arguments
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 214 rows containing missing values (position_quasirandom).
ggsave("iqa_3periodos.png",
       units = c("px"),
       width = 4500,
       height = 2993,
       plot = grid.arrange(iqa_p1, iqa_p2, iqa_p3, ncol = 3),
       path = "./graficos",
       dpi = 300,
       type = "cairo")
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 164 rows containing missing values (position_quasirandom).
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 253 rows containing missing values (position_quasirandom).
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 214 rows containing missing values (position_quasirandom).
## Using ragg device as default. Ignoring `type` and `antialias` arguments

7.1 Testando coisas

7.1.1 Correlação

parametros_IQA <- plan_wide_19902020 %>%
  select(CODIGO,
         pH,
         DBO,
         `Nitrogênio amoniacal`,
         `Nitrogênio total`,
         `Fósforo total`,
         `Temperatura água`,
         Turbidez,
         `Sólidos totais`,
         `Oxigênio dissolvido`,
         Condutividade)

write.csv(parametros_IQA,
          "./parametros_IQA.csv",
          row.names = FALSE)

parametros_IQA %>% 
  select(-CODIGO) %>% 
  ggcorr(method = "complete.obs",
           # "pearson",
           # "pairwise",
         name = "Correlação",
         label = TRUE,
         label_alpha = TRUE,
         digits = 3,
         low = "#3B9AB2",
         mid = "#EEEEEE",
         high = "#F21A00",
         # palette = "RdYlBu",
         layout.exp = 0,
         legend.position = "left",
         label_round = 3,
         )

# Gráfico das correlações entre todos os parâmetros com significância
# correl_IQA <- parametros_IQA %>% 
#   select(-CODIGO) %>% 
#   ggpairs(title = "Correlação entre parâmetros que compõem o IQA",
#           axisLabels = "show")

7.1.2 Condutividade elétrica

(cond_elet_p1 <- ggplot(plan_wide_19902020 %>% 
                          filter(ANO_COLETA>"1990" &
                                   ANO_COLETA<="2000"),
                        aes(CODIGO,
                            Condutividade))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=500,
            ymax=Inf,
            alpha=1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=-Inf,
            ymax=500,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "Condutividade elétrica no período 1990-2000",
        x="Estação",
        y="")+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_y_continuous(expand = expansion(mult = c(0.01, 0.05)),
                      n.breaks = 8,
                      limits = c(0,
                                 max(plan_wide_19902020$Condutividade, na.rm = TRUE)),
                      labels = scales::number_format(accuracy = 1,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
   scale_x_discrete(limits = c("87398500", "87398980", "87398900",
                               "87398950", "87405500", "87406900", "87409900"))+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme(
     plot.title = element_text(
       hjust = 0.5,
       color = "black",
       size = 19),
     axis.title.y = element_text(
       color = "black",
       size = 15),
     axis.text.y = element_text(
       color = "black",
       size = 17),
     axis.text.x = element_text(
       color = "black",
       size = 17),
   )
)

(cond_elet_p2 <- ggplot(plan_wide_19902020 %>% 
                          filter(ANO_COLETA>"2000" &
                                   ANO_COLETA<="2010"),
                        aes(CODIGO,
                            Condutividade))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=500,
            ymax=Inf,
            alpha=1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=-Inf,
            ymax=500,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "Condutividade elétrica no período 1990-2000",
        x="Estação",
        y="")+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_y_continuous(expand = expansion(mult = c(0.01, 0.05)),
                      n.breaks = 8,
                      limits = c(0,
                                 max(plan_wide_19902020$Condutividade, na.rm = TRUE)),
                      labels = scales::number_format(accuracy = 1,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
   scale_x_discrete(limits = c("87398500", "87398980", "87398900", 
                               "87398950", "87405500", "87406900", "87409900"))+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme(
     plot.title = element_text(
       hjust = 0.5,
       color = "black",
       size = 19),
     axis.title.y = element_text(
       color = "black",
       size = 15),
     axis.text.y = element_text(
       color = "black",
       size = 17),
     axis.text.x = element_text(
       color = "black",
       size = 17),
   )
)

(cond_elet_p3 <- ggplot(plan_wide_19902020 %>% 
                          filter(ANO_COLETA>"2010" &
                                   ANO_COLETA<="2020"),
                        aes(CODIGO,
                            Condutividade))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=500,
            ymax=Inf,
            alpha=1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=-Inf,
            ymax=500,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "Condutividade elétrica no período 1990-2000",
        x="Estação",
        y="")+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_y_continuous(expand = expansion(mult = c(0.01, 0.05)),
                      n.breaks = 8,
                      limits = c(0,
                                 max(plan_wide_19902020$Condutividade, na.rm = TRUE)),
                      labels = scales::number_format(accuracy = 1,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
   scale_x_discrete(limits = c("87398500", "87398980", "87398900",
                               "87398950", "87405500", "87406900", "87409900"))+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme(
     plot.title = element_text(
       hjust = 0.5,
       color = "black",
       size = 19),
     axis.title.y = element_text(
       color = "black",
       size = 15),
     axis.text.y = element_text(
       color = "black",
       size = 17),
     axis.text.x = element_text(
       color = "black",
       size = 17),
   )
)

grid.arrange(cond_elet_p1, cond_elet_p2, cond_elet_p3, ncol = 3)

(sum_cond_elet_p1 <- plan_wide_19902020 %>%
   select(CODIGO, Condutividade, ANO_COLETA) %>% 
   filter(ANO_COLETA>"1990" &
            ANO_COLETA<="2000") %>% 
   group_by(CODIGO) %>% 
   summarize(
     min = 
       min(Condutividade, 
           na.rm = TRUE),
     q1 = 
       quantile(Condutividade, 0.25, 
                na.rm = TRUE),
     median = 
       median(Condutividade, 
              na.rm = TRUE),
     mean = 
       mean(Condutividade, 
            na.rm= TRUE),
     q3 = 
       quantile(Condutividade, 0.75, 
                na.rm = TRUE),
     max = 
       max(Condutividade, 
           na.rm = TRUE))
)
## # A tibble: 7 x 7
##   CODIGO     min    q1 median  mean    q3   max
##   <chr>    <dbl> <dbl>  <dbl> <dbl> <dbl> <dbl>
## 1 87398500   9.4  51.1   67    75.1  83.2 340  
## 2 87398900  10    41.5   51    55.3  64.2 160  
## 3 87398950   9    41.5   51.5  60.1  69.5 160  
## 4 87398980  11.3  42.4   52.0  53.0  67.0  83.8
## 5 87405500  25    68.7   88.2 130.  170   560  
## 6 87406900  52    88.4  133.  193.  256.  576  
## 7 87409900  29    80    110.  134.  168.  460
(sum_cond_elet_p2 <- plan_wide_19902020 %>%
    select(CODIGO, Condutividade, ANO_COLETA) %>% 
    filter(ANO_COLETA>"2000" &
             ANO_COLETA<="2010") %>% 
    group_by(CODIGO) %>% 
    summarize(
      min = 
        min(Condutividade, 
            na.rm = TRUE),
      q1 = 
        quantile(Condutividade, 0.25, 
                 na.rm = TRUE),
      median = 
        median(Condutividade, 
               na.rm = TRUE),
      mean = 
        mean(Condutividade, 
             na.rm= TRUE),
      q3 = 
        quantile(Condutividade, 0.75, 
                 na.rm = TRUE),
      max = 
        max(Condutividade, 
            na.rm = TRUE))
)
## # A tibble: 7 x 7
##   CODIGO     min    q1 median  mean    q3   max
##   <chr>    <dbl> <dbl>  <dbl> <dbl> <dbl> <dbl>
## 1 87398500  11.9  67.0   82.6  84.8 102.   164.
## 2 87398900  11    44.4   52.3  57.1  72.6  136.
## 3 87398950  39.8  58.4   76    82.3  98.3  160 
## 4 87398980   9.4  42.4   49.7  51.5  62    114.
## 5 87405500  17    77.5  107   142.  171.   679 
## 6 87406900  23.1  85.6  124.  164.  199.   619 
## 7 87409900  56.1 114.   177   200.  242    454
(sum_cond_elet_p3 <- plan_wide_19902020 %>%
    select(CODIGO, Condutividade, ANO_COLETA) %>% 
    filter(ANO_COLETA>"2010" &
             ANO_COLETA<="2020") %>% 
    group_by(CODIGO) %>% 
    summarize(
      min = 
        min(Condutividade, 
            na.rm = TRUE),
      q1 = 
        quantile(Condutividade, 0.25, 
                 na.rm = TRUE),
      median = 
        median(Condutividade, 
               na.rm = TRUE),
      mean = 
        mean(Condutividade, 
             na.rm= TRUE),
      q3 = 
        quantile(Condutividade, 0.75, 
                 na.rm = TRUE),
      max = 
        max(Condutividade, 
            na.rm = TRUE),
      n = 
        length(Condutividade))
)
## # A tibble: 7 x 8
##   CODIGO     min    q1 median  mean    q3   max     n
##   <chr>    <dbl> <dbl>  <dbl> <dbl> <dbl> <dbl> <int>
## 1 87398500  0.01  68.5   80.2  80.4  99.5 125.     34
## 2 87398900 39.7   53.4   58.3  61.1  65.5 103      36
## 3 87398950 40.9   64.7   70.1  76.1  82.5 195.     35
## 4 87398980 43.2   51.7   54.0  56.3  61.0  78.9    28
## 5 87405500 47     85.8  121.  146.  209.  286      33
## 6 87406900 62.7   95.9  142.  163.  216.  354.     35
## 7 87409900 65.7  121.   159.  179.  245.  498.     37
# plan_wide_19902020 %>% 
#    select(CODIGO, IQA) %>% 
#    group_by(CODIGO) %>% 
#    summarize(
#       min = 
#          min(IQA, 
#              na.rm = TRUE),
#       q1 = 
#          quantile(IQA, 0.25, 
#                   na.rm = TRUE),
#       median = 
#          median(IQA, 
#                 na.rm = TRUE),
#       mean = 
#          mean(IQA, 
#               na.rm= TRUE),
#       q3 = 
#          quantile(IQA, 0.75, 
#                   na.rm = TRUE),
#       max = 
#          max(IQA, 
#              na.rm = TRUE))
ggsave("cond_elet_p1.png",
       plot = cond_elet_p1,
       path = "./graficos",
       dpi = 300,
       type = "cairo")
## Saving 10 x 6.66 in image
## Warning: Using ragg device as default. Ignoring `type` and `antialias` arguments
## Warning: Removed 15 rows containing non-finite values (stat_boxplot).
## Removed 15 rows containing non-finite values (stat_boxplot).
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 15 rows containing missing values (position_quasirandom).
ggsave("cond_elet_p2.png",
       plot = cond_elet_p2,
       path = "./graficos",
       dpi = 300,
       type = "cairo")
## Saving 10 x 6.66 in image
## Warning: Using ragg device as default. Ignoring `type` and `antialias` arguments
## Warning: Removed 37 rows containing non-finite values (stat_boxplot).
## Removed 37 rows containing non-finite values (stat_boxplot).
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 37 rows containing missing values (position_quasirandom).
ggsave("cond_elet_p3.png",
       plot = cond_elet_p3,
       path = "./graficos",
       dpi = 300,
       type = "cairo")
## Saving 10 x 6.66 in image
## Warning: Using ragg device as default. Ignoring `type` and `antialias` arguments
## Warning: Removed 25 rows containing non-finite values (stat_boxplot).
## Removed 25 rows containing non-finite values (stat_boxplot).
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 25 rows containing missing values (position_quasirandom).
ggsave("cond_elet_3periodos.png",
       units = c("px"),
       width = 4500,
       height = 2993,
       plot = grid.arrange(cond_elet_p1, cond_elet_p2, cond_elet_p3, ncol = 3),
       path = "./graficos",
       dpi = 300,
       type = "cairo")
## Warning: Removed 15 rows containing non-finite values (stat_boxplot).
## Warning: Removed 15 rows containing non-finite values (stat_boxplot).
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 15 rows containing missing values (position_quasirandom).
## Warning: Removed 37 rows containing non-finite values (stat_boxplot).
## Removed 37 rows containing non-finite values (stat_boxplot).
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 37 rows containing missing values (position_quasirandom).
## Warning: Removed 25 rows containing non-finite values (stat_boxplot).
## Removed 25 rows containing non-finite values (stat_boxplot).
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 25 rows containing missing values (position_quasirandom).
## Warning: Using ragg device as default. Ignoring `type` and `antialias` arguments

---
title: "TCC"
author: "Leonardo Fernandes Wink"
date: "`r format(Sys.time(), '%d/%m/%Y')`"
output:
  html_document: 
    highlight: haddock
    keep_md: yes
    number_sections: yes
    theme: flatly
    toc: yes
    toc_float:
      collapsed: no
      smooth_scroll: no
    fig_width: 10
    fig_height: 6.66
    fig_caption: yes
    code_download: true
  pdf_document:
    toc: yes
  word_document: 
    toc: yes
    keep_md: yes
always_allow_html: yes
editor_options: 
  chunk_output_type: console
fig.align: center
---

```{r Rotina pra toda vez que abrir o documento, echo = FALSE}
# Abrir o GitHub Desktop
# Abrir o RStudio
# Verificar se estão 
```

# Anotações de coisas por fazer:

* Descobrir como colocar as estações no sentido correto montante -> jusante nos sumários
* not entirely necessary pq tenho os sumários mais aprimorados ainda no excel.

> 87398500, 87398980, 87398900, 87398950, 87405500, 87406900, 87409900

* ~~Aprender a segmentar o meu dataset por períodos~~
* ~~aprender a criar uma nova coluna com a segmentação dos períodos~~
* aprender a colocar a legenda dentro do gráfico
  * reduzir o tamanho da legenda
* ~~corrigir os valores 0 de IQA pra NA~~
* descobrir como conseguir a equação do lm
* ~~aprender a pivotar o sumário~~ -> meu sumário do google docs ta batendo direitinho com o do R
* descobrir se há outros TCCs com disponibilização de códigos
* correlação forte entre condutividade e Namon/Ptot/DBO



| 1990-2000 | 2000-2010 | 2010-2020 |
| :-------: | :-------: | :-------: |
| 1990-2000 | 2000-2010 | 2010-2020 |



# Instalar os pacotes
```{r instalar pacotes}
# install.packages(tidyverse)
```

## acessar os pacotes
```{r Acessar os pacotes, message = FALSE, warning = TRUE}
# library(readr)
# library(rmarkdown)
# # library(qboxplot)
# library(readxl)
# library(pillar)
# library(dplyr)
# library(tidyverse)
# library(gapminder)
# library(knitr)
# library(kableExtra)
# library(ggpubr)
# library(gridExtra)
# library(modelsummary)
# library(gtsummary)
# library(GGally)
pacman::p_load(readr, rmarkdown, readxl,
               pillar, dplyr, tidyverse,
               gapminder, knitr, kableExtra,
               gridExtra, #modelsummary, 
               gtsummary, ggplot2,
               ggbeeswarm, GGally)
# pacman::p_load(tibbletime)
```

```{cronometrando quanto tempo cada chunk leva}
knitr::knit_hooks$set(time_it = local({
  now <- NULL
  function(before, options) {
    if (before) {
      # record the current time before each chunk
      now <<- Sys.time()
    } else {
      # calculate the time difference after a chunk
      res <- difftime(Sys.time(), now)
      # return a character string to show the time
      paste("Time for this code chunk to run:", res)
    }
  }
}))

knitr::opts_chunk$set(time_it = TRUE)
```

## importando a planilha
```{r Importando a planilha, echo = FALSE, message = TRUE, warning = FALSE}
plan_wide_19902020 <- read_delim("https://raw.githubusercontent.com/leonardofwink/TCC_gh/main/plan_wide_19902020.tsv",
                                 delim = "\t", 
                                 escape_double = FALSE,
                                 col_types = cols(
                                   Alcalinidade = col_double(),
                                   CODIGO = col_character(), 
                                   COORD_GEO_LAT_GRAU = col_double(),
                                   COORD_GEO_LONG_GRAU = col_double(),
                                   DATA_COLETA = col_date(format = "%d/%m/%Y"),
                                   Nitrato = col_double(), 
                                   Nitrito = col_double(),
                                   SDT = col_double(), 
                                   SST = col_double(),
                                   `Vazao` = col_double(), 
                                   `Vazao rio` = col_double()
                                 ),
                                 locale = locale(
                                   date_names = "pt", 
                                   decimal_mark = ",",
                                   grouping_mark = ""
                                 ),
                                 trim_ws = TRUE
)

# teste[~'2000']
# 
# teste <- plan_wide_19902020 %>%
#   dplyr::filter(DATA_COLETA >= as.POSIXct("2010-01-01")) #this works
# 
# teste$DATA_COLETA <- as.POSIXct(teste$DATA_COLETA)
# 
# teste %>% 
#   dplyr::arrange(DATA_COLETA)
# teste %>% 
#   filter_time(time_formula = '2013-01-01' ~ '2020-12-31')
# 
# 
# typeof(teste$DATA_COLETA)
# 
#   as_tbl_time(plan_wide_19902020, index = DATA_COLETA)
# str(plan_wide_19902020$DATA_COLETA)
```

```{r Mostrando os dados, layout="l-body-outset", echo = FALSE}
# library(rmarkdown)
# kable(head(plan_wide_19902020),format = "html") %>% 
#   kable_styling(full_width = FALSE,
#                 bootstrap_options = c("striped", "hover", "condensed", "responsive"))
```

```{r Visualização da planilha importada, echo = FALSE}
paged_table(plan_wide_19902020,
           options = list(rows.print = 15,
                          cols.print = 10))
```

# data wrangling

```{r data wrangling}
plan_wide_19902020 <- plan_wide_19902020 %>% 
  mutate(IQA = ifelse(IQA == 0, NA, IQA))
```

```{r Códigos Git, echo = FALSE}
# cd myrepo
# ls
# head README.md
# git status
# git add README.md
# git commit -m "A commit from my local computer"
# 
# cd .. # voltar pro diretório acima
# rm -rf myrepo/ #remover/apagar a pasta myrepo
```

```{r Aprendendo Git, echo = FALSE}
# slides da bia que ajudam mt
# https://beatrizmilz.github.io/slidesR/git_rstudio/11-2021-ENCE.html#20
# aprendendo a sincronizar usando esse guia -> 
# https://happygitwithr-com.translate.goog/push-pull-github.html?_x_tr_sl=auto&_x_tr_tl=pt&_x_tr_hl=pt-BR
# library(usethis)
# usethis::create_github_token() criar um código pra acesso e sincronização between R e github

# gitcreds::gitcreds_set() 
# 
# use_git_config(user.name = "leonardofwink",
#                user.email = "leonardofwink@gmail.com")
# usethis::gh_token_help()

# Como mostrar os dados de um arquivo via Git/GitHub
# git clone https://github.com/leonardofwink/myrepo.git
# cd myrepo #acessa a pasta myrepo
# ls #lista os arquivos da pasta 
# head README.md #mostra as primeiras observações do arquivo

# Como mostrar os dados de um arquivo via R
# head(C:/Users/Léo/myrepo/README.md)

# Adicionar uma linha ao README.md e verificar se o Git percebe a mudança
# echo "A line I wrote on my local computer" >> README.md
# git status
## C:\Users\Léo\myrepo>git status
## On branch main
## Your branch is up to date with 'origin/main'.
## 
## Changes not staged for commit:
##   (use "git add <file>..." to update what will be committed)
##   (use "git restore <file>..." to discard changes in working directory)
##         **modified:   README.md**
## 
## no changes added to commit (use "git add" and/or "git commit -a")
```

# setting theme
```{r setting theme}
theme_grafs <- function(bg = "white", 
                        coloracao_letra = "black") {
  theme(
    plot.title = element_text(
      hjust = 0.5,
      color = coloracao_letra,
      size = 19),
    
    axis.title.x = 
      # element_text(
      # color = coloracao_letra,
      # size = 15,
      # angle = 0,),
      element_blank(),
    axis.title.y = element_text(
      color = coloracao_letra,
      size = 15,
      angle = 90),
    
    axis.text.x = element_text(
      color = coloracao_letra,
      size = 17),
    axis.text.y = element_text(
      color = coloracao_letra,
      size = 17,
      angle = 0),
    
    panel.background = element_rect(fill = bg),
    plot.background = element_rect(fill = bg),
    plot.margin = margin(l = 5, r = 10,
                         b = 5, t = 5)
  )
}
```

# setting periodos
```{r setting periodos}
# p1 <- plan_wide_19902020 %>% 
#   filter(ANO_COLETA > "1990" &
#            ANO_COLETA <= "2000")
# 
# p2 <- plan_wide_19902020 %>% 
#   filter(ANO_COLETA > "2000" &
#            ANO_COLETA <= "2010")
# 
# p3 <- plan_wide_19902020 %>% 
#   filter(ANO_COLETA > "2010" &
#            ANO_COLETA <= "2020")

# teste_all_periodos <- plan_wide_19902020 %>% 
#   filter(
#     between(ANO_COLETA, 1990, 2000)
#   )
```

# setting sumaries
```{r Sumários}
p1 <- plan_wide_19902020 %>% 
  filter(ANO_COLETA > "1990" &
           ANO_COLETA <= "2000")

p2 <- plan_wide_19902020 %>%
  filter(ANO_COLETA > "2000" &
         ANO_COLETA <= "2010")

p3 <- plan_wide_19902020 %>%
  filter(ANO_COLETA > "2010" &
         ANO_COLETA <= "2020")

  # periodo = c(p1 <- plan_wide_19902020 %>% 
  #   filter(ANO_COLETA > "1990" &
  #            ANO_COLETA <= "2000"),
  # 
  # p2 <- plan_wide_19902020 %>%
  #   filter(ANO_COLETA > "2000" &
  #            ANO_COLETA <= "2010"),
  # 
  # p3 <- plan_wide_19902020 %>%
  #   filter(ANO_COLETA > "2010" &
  #            ANO_COLETA <= "2020"))

# sumario <- function(parametros = parametros, periodo){
#   plan_wide_19902020 %>%
#    select(CODIGO, ., ANO_COLETA) %>% 
#    # filter(ANO_COLETA>"1990" &
#    #          ANO_COLETA<="2000") %>% 
#    group_by(CODIGO) %>% 
#    summarize(
#      min = 
#        min(parametros, 
#            na.rm = TRUE),
#      q1 = 
#        quantile(parametros, 0.25, 
#                 na.rm = TRUE),
#      median = 
#        median(parametros, 
#               na.rm = TRUE),
#      mean = 
#        mean(parametros, 
#             na.rm= TRUE),
#      q3 = 
#        quantile(parametros, 0.75, 
#                 na.rm = TRUE),
#      max = 
#        max(parametros, 
#            na.rm = TRUE))
# }

# plan_wide_19902020 %>% 
#   sumario(parametros = DBO)

# sum_IQA_p1 <- plan_wide_19902020 %>%
#    select(CODIGO, IQA, ANO_COLETA) %>% 
#    filter(ANO_COLETA>"1990" &
#             ANO_COLETA<="2000") %>% 
#    group_by(CODIGO) %>% 
#    summarize(
#      min = 
#        min(IQA, 
#            na.rm = TRUE),
#      q1 = 
#        quantile(IQA, 0.25, 
#                 na.rm = TRUE),
#      median = 
#        median(IQA, 
#               na.rm = TRUE),
#      mean = 
#        mean(IQA, 
#             na.rm= TRUE),
#      q3 = 
#        quantile(IQA, 0.75, 
#                 na.rm = TRUE),
#      max = 
#        max(IQA, 
#            na.rm = TRUE))
```


# Parâmetros físico-químicos

### Oxigênio Dissolvido

```{r setting od}
# par_od <- plan_wide_19902020 %>% 
#   select(CODIGO, `Oxigênio dissolvido`) %>% 
#   group_nest(CODIGO)

# parametros_IQA

# parametros <- colnames(parametros_IQA)[]

plan_wide_19902020 %>%
  ggplot(
    aes(CODIGO, `Oxigênio dissolvido`)
  )+
  annotate("rect",
           xmin = -Inf, xmax = Inf,
           ymin = -Inf, ymax = 2,
           alpha = 1,
           fill = "#ac5079")+ # >pior classe
  annotate("rect",
           xmin = -Inf, xmax = Inf,
           ymin = 2, ymax = 4,
           alpha = 1,
           fill = "#eb5661")+ #classe 4
  annotate("rect",
           xmin = -Inf, xmax = Inf,
           ymin = 4, ymax = 5,
           alpha=1,
           fill="#fcf7ab")+ #classe 3
  annotate("rect",
           xmin=-Inf,
           xmax=Inf,
           ymin=5,
           ymax=6,
           alpha=1,
           fill="#70c18c")+ #classe 2
  annotate("rect",
           xmin=-Inf,
           xmax=Inf,
           ymin=6,
           ymax=Inf,
           alpha=1,
           fill="#8dcdeb")+ #classe 1
  stat_boxplot(
    geom = 'errorbar',
    width=0.3,
    position = position_dodge(width = 0.65)
  )+
  geom_boxplot(
    fill = '#F8F8FF',
    color = "black",
    outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
    width= 0.7
  )+
  labs(
    title = "Oxigênio Dissolvido no período 1990-2000",
    x = "Estação",
    y = "mg/L"
  )+
  ggbeeswarm::geom_quasirandom(
    size = 1.2,
    alpha = .25,
    width = .07,
  )+
  scale_y_continuous(
    expand = expansion(mult = c(0,0)),
    n.breaks = 11,
    limits = c(-1,21)
  )+
  scale_x_discrete(limits = c("87398500",
                              "87398980",
                              "87398900",
                              "87398950",
                              "87405500",
                              "87406900",
                              "87409900"),
                   labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
  )+
  geom_smooth(method = "lm",
              se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
              aes(group=1),
              alpha=.5,
              na.rm = TRUE,
              size = 1)



```


```{r Gráfico OD periodo 1, echo = FALSE, warning=FALSE, message = FALSE, fig.cap="Oxigênio Dissolvido no período 1990-2000"}
(od_p1 <- ggplot(plan_wide_19902020 %>% 
                   filter(ANO_COLETA > "1990" &
                          ANO_COLETA <= "2000"),
                 aes(CODIGO,
                     `Oxigênio dissolvido`))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=-Inf,
            ymax=2,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=2,
            ymax=4,
            alpha=1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=4,
            ymax=5,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=5,
            ymax=6,
            alpha=1,
            fill="#70c18c")+ #classe 2
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=6,
            ymax=Inf,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "Oxigênio Dissolvido no período 1990-2000",
        x="Estação",
        y="mg/L")+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_y_continuous(
     expand = expansion(mult = c(0,0)),
     n.breaks = 11,
     limits = c(-1,21))+
   scale_x_discrete(limits = c("87398500", 
                               "87398980", 
                               "87398900", 
                               "87398950", 
                               "87405500", 
                               "87406900", 
                               "87409900"),
                    labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
   )+
   geom_smooth(
     method = "lm",
     se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
     aes(group=1),
     alpha=.5,
     na.rm = TRUE,
     size = 1
   )+
  theme_grafs()
)
```

```{r Gráfico OD periodo 2, echo = FALSE, warning=FALSE, message = FALSE}
(od_p2 <-ggplot(plan_wide_19902020 %>% 
                  filter(ANO_COLETA>"2000" &
                           ANO_COLETA<="2010"),
                aes(CODIGO,
                    `Oxigênio dissolvido`))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=-Inf,
            ymax=2,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=2,
            ymax=4,
            alpha=1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=4,
            ymax=5,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=5,
            ymax=6,
            alpha=1,
            fill="#70c18c")+ #classe 2
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=6,
            ymax=Inf,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "Oxigênio Dissolvido no período 2000-2010",
        x="Estação",
        y="")+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_y_continuous(expand = expansion(mult = c(0,0)),
                      n.breaks = 11,
                      limits = c(-1,21))+
   scale_x_discrete(limits = c("87398500", "87398980", "87398900", 
                               "87398950", "87405500", "87406900", "87409900"))+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
 theme_grafs()
)
```

```{r Gráfico OD periodo 3, echo = FALSE, warning=FALSE, message = FALSE}
(od_p3 <-ggplot(plan_wide_19902020 %>% 
                  filter(ANO_COLETA>"2010" &
                           ANO_COLETA<="2020"),
                aes(CODIGO,
                    `Oxigênio dissolvido`))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=-Inf,
            ymax=2,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=2,
            ymax=4,
            alpha=1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=4,
            ymax=5,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=5,
            ymax=6,
            alpha=1,
            fill="#70c18c")+ #classe 2
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=6,
            ymax=Inf,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "Oxigênio Dissolvido no período 2010-2020",
        x="",
        y="mg/L")+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_y_continuous(expand = expansion(mult = c(0,0)),
                      n.breaks = 11,
                      limits = c(-1,21))+
   scale_x_discrete(limits = c("87398500", "87398980", "87398900", 
                               "87398950", "87405500", "87406900", "87409900"))+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
)
```

```{r Gráfico OD 3 periodos juntos, echo = TRUE, warning=FALSE, message = FALSE, fig.cap="Oxigênio Dissolvido no período 1990-2020"}
grid.arrange(od_p1, od_p2, od_p3, ncol = 3)
```

```{r Salvando OD}
ggsave("od_p1.png",
       plot = od_p1,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("od_p2.png",
       plot = od_p2,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("od_p3.png",
       plot = od_p3,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("od_3periodos_2.png",
       units = c("px"),
       width = 4500,
       height = 2993,
       plot = grid.arrange(od_p1, od_p2, od_p3, ncol = 3),
       path = "./graficos",
       dpi = 300,
       type = "cairo")
```

```{r Gráfico OD_chernobyl, echo = FALSE, warning=FALSE, message = FALSE}
# p1 <- function(plan_wide_19902020, ANO_COLETA) {
#   plan_wide_19902020 %>% 
#     filter(ANO_COLETA > "1990" &
#            ANO_COLETA <= "2000")
# }
# 
# 
# classes_od <- function(plan_wide_19902020, parametro, periodo){
#   ggplot(plan_wide_19902020 %>%
#            periodo),
#   aes(CODIGO,
#       parametro)
# }


# (od_chernobyl <- ggplot(plan_wide_19902020 %>%
#                           p1(ANO_COLETA > "1990" &
#                                ANO_COLETA <= "2000"),
#                         aes(CODIGO,
#                             `Oxigênio dissolvido`))+
#     annotate("rect",
#              xmin=-Inf,
#              xmax=Inf,
#              ymin=-Inf,
#              ymax=2,
#              alpha=1,
#              fill="#ac5079")+ #>pior classe
#     annotate("rect",
#              xmin=-Inf,
#              xmax=Inf,
#              ymin=2,
#              ymax=4,
#              alpha=1,
#              fill="#eb5661")+ #classe 4
#     annotate("rect",
#              xmin=-Inf,
#              xmax=Inf,
#              ymin=4,
#              ymax=5,
#              alpha=1,
#              fill="#fcf7ab")+ #classe 3
#     annotate("rect",
#              xmin=-Inf,
#              xmax=Inf,
#              ymin=5,
#              ymax=6,
#              alpha=1,
#              fill="#70c18c")+ #classe 2
#     annotate("rect",
#              xmin=-Inf,
#              xmax=Inf,
#              ymin=6,
#              ymax=Inf,
#              alpha=1,
#              fill="#8dcdeb")+ #classe 1
#     stat_boxplot(geom = 'errorbar',
#                  width=0.3,
#                  position = position_dodge(width = 0.65))+
#     geom_boxplot(fill='#F8F8FF',
#                  color="black",
#                  outlier.shape = NA, #se deixar NA fica só o jitter, se não, deixa 1
#                  width= 0.7)+
#     labs(title = "Oxigênio Dissolvido no período 1990-2000",
#          x="Estação",
#          y="mg/L")+
#     # geom_jitter(width = .07,
#     #             alpha=.15,
#     #             size=1.,
#     #             color="black")+
#     ggbeeswarm::geom_quasirandom(
#       size = 1.2,
#       alpha = .25,
#       width = .07,
#     )+
#     scale_y_continuous(expand = expansion(mult = c(0,0)),
#                        n.breaks = 11,
#                        limits = c(-1,21))+
#     scale_x_discrete(limits = c("87398500",
#                                 "87398980",
#                                 "87398900",
#                                 "87398950",
#                                 "87405500",
#                                 "87406900",
#                                 "87409900"),
#                      labels = c("PM1", "PM2", "PM3", "PM4", "PM5", "PM6", "PM7")
#     )+
#     geom_smooth(method = "lm",
#                 se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
#                 aes(group=1),
#                 alpha=.5,
#                 na.rm = TRUE,
#                 size = 1)+
#     # geom_line(
#     #   aes(color="red"),
#     #   alpha=.0)+
#     # scale_color_manual("Legenda",
#     #                    guide="legend",
#     #                    values = c("Classe 1"="#8dcdeb",
#     #                               "Classe 2"="#70c18c",
#     #                               "Classe 3"="#fcf7ab",
#     #                               "Classe 4"="#eb5661",
#     #                               "Pior Classe"="#ac5079"))+
#     # guides(color=guide_legend(override.aes = list(linetype=c(1,1,1,1,1),
#   #                                               lwd=c(2,2,2,2,2),
#   #                                               shape=c(NA,NA,NA,NA,NA),
#   #                                               alpha=1)))+
#   theme(
#     plot.title = element_text(size = 19),
#     axis.title.y = element_text(size = 15),
#     axis.text.y = element_text(size = 17),
#     axis.text.x = element_text(size = 17),
#   )
# )
```

```{r mais um teste, echo = FALSE, warning=FALSE, message = FALSE}
# install.packages("modelsummary")
# library(modelsummary)
# url111 <- 'https://vincentarelbundock.github.io/Rdatasets/csv/HistData/Guerry.csv'
# dat <- read.csv(url111)
# dat$Small <- dat$Pop1831 > median(dat$Pop1831)
# datasummary_skim(dat)
# datasummary_balance(~Small, dat)
# datasummary_correlation(dat)
# 
# 
# datasummary_correlation(parametros_IQA)
# datasummary(Literacy + Commerce~Small *(mean+sd), dat)
# mod <- lm(Donations~Crime_prop, data = dat)
# 
# mod2 <-lm(data = plan_wide_19902020,
#           plan_wide_19902020$CODIGO ~ list(parametros_IQA))
# 
# for (i in 1:lenght(parametros_IQA)){
#   lm(plan_wide_19902020$CODIGO ~ parametros_IQA[i])
# }
# 
# training_data <- as.data.frame(matrix(sample(1:64), nrow = 8))
# colnames(training_data) <- c("independent_variable", paste0("x", 1:7))
# Vars <- as.list(c("x1+x2+x3",
#                 "x1+x2+x4",
#                 "x1+x2+x5",
#                 "x1+x2+x6",
#                 "x1+x2+x7"))
# allModelsList <- lapply(paste("independent_variable ~", Vars), as.formula)
# allModelsResults <- lapply(allModelsList, function(x) lm(x, data = training_data))
# allModelsSummaries = lapply(allModelsResults, summary) 
# allModelsSummaries[[1]]$r.squared
# 
# 
# 
# 
# 
# modelsummary(mod)
# 
# plan_wide_19902020 %>%
#   select(CODIGO,
#          DBO,
#          `Oxigênio dissolvido`,
#          `Fósforo total`,
#          `Escherichia coli`,
#          `Nitrogênio amoniacal`,
#          pH,
#          Turbidez,
#          `Sólidos totais`,
#          `Temperatura água`) %>%
#   datasummary_skim()
# 
# plan_wide_19902020 %>%
#   select(CODIGO,
#          `Oxigênio dissolvido`) %>%
#   datasummary_skim()

# install.packages("estimatr")
# library(estimatr)

# glimpse(plan_wide_19902020)


# datasummary(
#   unique(CODIGO) + `Oxigênio dissolvido` ~
#     P25 + Mean,data = plan_wide_19902020 %>%
#     select(CODIGO,
#            `Oxigênio dissolvido`)
# )
# install.packages("gtsummary", type = "binary")
# library(gtsummary)



# parametros_IQA <- plan_wide_19902020 %>%
#   select(CODIGO,
#          `Escherichia coli`,
#          pH,
#          DBO,
#          `Nitrogênio amoniacal`,
#          `Nitrogênio total`,
#          `Fósforo total`,
#          `Temperatura água`,
#          Turbidez,
#          `Sólidos totais`,
#          `Oxigênio dissolvido`,
#          Condutividade)
# 
# 
# mod <- lm(y ~ x, cars)
# modelsummary(cars)

# fit<-lm(plan_wide_19902020$CODIGO~plan_wide_19902020$DBO)
# summary(fit)
# ggplot(plan_wide_19902020,
#        aes(x=CODIGO,
#            y=DBO,
#            add = "reg.line"))+
#   geom_smooth(method = "lm",
#                se = TRUE, #se deixar TRUE gera o intervalo de confiança de 95%
#                aes(group = 1),
#                alpha= .8,
#                na.rm = TRUE,
#                size = 1)+
#   geom_point(na.rm = TRUE)+
#   stat_regline_equation(aes(label = ..eq.label..),
#                         na.rm = TRUE,
#                         label.x.npc = "center",
#                         label.y.npc = "center",
#                         geom = "text")
#   
# 
# summary(fit)
```

```{r Gráfico IQA OD periodo1, echo = FALSE, message=FALSE, warning=FALSE}
(iqaod_p1 <-ggplot(plan_wide_19902020 %>% 
                     filter(ANO_COLETA > "1990" &
                              ANO_COLETA <= "2000"),
                   aes(CODIGO,
                       IQA_OD, na.rm = TRUE))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=-Inf,
            ymax=19,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=19,
            ymax=36,
            alpha=1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=36,
            ymax=51,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=51,
            ymax=79,
            alpha=1,
            fill="#70c18c")+ #classe 2
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=79,
            ymax=Inf,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65),
                na.rm = TRUE)+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7,
                na.rm = TRUE)+
   labs(title = "Variação do IQA para o parâmetro Oxigênio Dissolvido 1990-2000",
        x="Estação",
        y="")+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_y_continuous(expand = expansion(mult = c(0,0)),
                      n.breaks = 6,
                      limits = c(-1,101))+
   scale_x_discrete(limits = c("87398500", "87398980", "87398900", "87398950", "87405500", "87406900", "87409900"))+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs())
```

```{r Gráfico IQA OD periodo2, echo = FALSE, warning= FALSE, message = FALSE}
(iqaod_p2 <-ggplot(plan_wide_19902020 %>% 
                     filter(ANO_COLETA > "2000" &
                              ANO_COLETA <= "2010"),
                   aes(CODIGO,
                       IQA_OD, na.rm = TRUE))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=-Inf,
            ymax=19,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=19,
            ymax=36,
            alpha=1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=36,
            ymax=51,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=51,
            ymax=79,
            alpha=1,
            fill="#70c18c")+ #classe 2
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=79,
            ymax=Inf,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65),
                na.rm = TRUE)+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7,
                na.rm = TRUE)+
   labs(title = "Variação do IQA para o parâmetro Oxigênio Dissolvido 2000-2010",
        x="Estação",
        y="")+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_y_continuous(expand = expansion(mult = c(0,0)),
                      n.breaks = 6,
                      limits = c(-1,101))+
   scale_x_discrete(limits = c("87398500", "87398980", "87398900", "87398950", "87405500", "87406900", "87409900"))+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs())

```

```{r Gráfico IQA OD periodo3, echo = FALSE, warning=FALSE, message = FALSE}
(iqaod_p3 <-ggplot(plan_wide_19902020 %>% 
                     filter(ANO_COLETA > "2010" &
                              ANO_COLETA <= "2020"),
                   aes(CODIGO,
                       IQA_OD, na.rm = TRUE))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=-Inf,
            ymax=19,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=19,
            ymax=36,
            alpha=1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=36,
            ymax=51,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=51,
            ymax=79,
            alpha=1,
            fill="#70c18c")+ #classe 2
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=79,
            ymax=Inf,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65),
                na.rm = TRUE)+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7,
                na.rm = TRUE)+
   labs(title = "Variação do IQA para o parâmetro Oxigênio Dissolvido 2010-2020",
        x="Estação",
        y="")+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_y_continuous(expand = expansion(mult = c(0,0)), #expandir o limite max de visualização do dado em x%
                      n.breaks = 6,
                      limits = c(-1,101))+
   scale_x_discrete(limits = c("87398500", "87398980", "87398900", "87398950", "87405500", "87406900", "87409900"))+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs())
```

```{r Gráfico OD_IQA 6 periodos juntos, warning=FALSE, message=FALSE}
grid.arrange(iqaod_p1, iqaod_p2, iqaod_p3, ncol = 3)
```

Resultado que quero chegar pro sumário

| variable  | Estação 1 | Estação 2 |  Estação 3 |  Estação 4 |
| :-------: | :-------: | :-------: |  :-------: |  :-------: |
| max       | 15        |   17      |     16     |    14      |
| med       | 14        |   16      |     15     |    13      |
| min       | 13        |   15      |     14     |    12      |
| n         | 15        |   12      |     3      |    4       |

```{r Sumário OD, echo = FALSE}
plan_wide_19902020 %>%
  select(CODIGO, `Oxigênio dissolvido`, ANO_COLETA) %>% 
  filter(ANO_COLETA>"1990" &
           ANO_COLETA<="2000") %>% 
  group_by(CODIGO) %>% 
  summarize(
    min = 
      min(`Oxigênio dissolvido`, na.rm = TRUE),
    q1 = 
      quantile(`Oxigênio dissolvido`, 0.25, na.rm = TRUE),
    median = 
      median(`Oxigênio dissolvido`, na.rm = TRUE),
    mean = 
      mean(`Oxigênio dissolvido`, na.rm= TRUE),
    q3 = 
      quantile(`Oxigênio dissolvido`, 0.75, na.rm = TRUE),
    max = 
      max(`Oxigênio dissolvido`, na.rm = TRUE))

plan_wide_19902020 %>%
  select(CODIGO, `Oxigênio dissolvido`, ANO_COLETA) %>% 
  filter(ANO_COLETA>"2000" &
           ANO_COLETA<="2010") %>% 
  group_by(CODIGO) %>% 
  summarize(
    min = 
      min(`Oxigênio dissolvido`, na.rm = TRUE),
    q1 = 
      quantile(`Oxigênio dissolvido`, 0.25, na.rm = TRUE),
    median = 
      median(`Oxigênio dissolvido`, na.rm = TRUE),
    mean = 
      mean(`Oxigênio dissolvido`, na.rm= TRUE),
    q3 = 
      quantile(`Oxigênio dissolvido`, 0.75, na.rm = TRUE),
    max = 
      max(`Oxigênio dissolvido`, na.rm = TRUE))

plan_wide_19902020 %>%
  select(CODIGO, `Oxigênio dissolvido`, ANO_COLETA) %>% 
  filter(ANO_COLETA>"2010" &
           ANO_COLETA<="2020") %>% 
  group_by(CODIGO) %>% 
  summarize(
    min = 
      min(`Oxigênio dissolvido`, na.rm = TRUE),
    q1 = 
      quantile(`Oxigênio dissolvido`, 0.25, na.rm = TRUE),
    median = 
      median(`Oxigênio dissolvido`, na.rm = TRUE),
    mean = 
      mean(`Oxigênio dissolvido`, na.rm= TRUE),
    q3 = 
      quantile(`Oxigênio dissolvido`, 0.75, na.rm = TRUE),
    max = 
      max(`Oxigênio dissolvido`, na.rm = TRUE))

# sumario_OD3 <- plan_wide_19902020 %>%
#   select(DATA_COLETA, CODIGO, `Oxigênio dissolvido`) %>% 
#   pivot_wider(id_cols = DATA_COLETA,
#               names_from = CODIGO,
#               values_from = plan_wide_19902020$`Oxigênio dissolvido`)
# 
# unique(plan_wide_19902020$CODIGO)

# 
#   pivot_wider(id_cols = CODIGO,
#               names_from = CODIGO,
#               values_from = `Oxigênio dissolvido`)
# 
# 
#   group_by(CODIGO) %>%
#   get_summary_stats(type = "common") %>%
#   pivot_wider(id_cols = variable,
#               names_from = CODIGO,
#               values_from = variable$`Oxigênio dissolvido`)
# 
# # install.packages("ggpubr")
# # library(ggpubr)
```

```{r setup, include=FALSE}
# knitr::opts_chunk$set(echo = TRUE)
```

### Demanda Bioquímica de Oxigênio

```{r Gráfico DBO período1, echo = FALSE, warning = FALSE, message = FALSE}
(dbo_p1<-ggplot(plan_wide_19902020 %>% 
                  filter(ANO_COLETA>"1990" &
                           ANO_COLETA<="2000"),
                aes(CODIGO,
                    DBO))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=10,
            ymax=Inf,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=5,
            ymax=10,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=3,
            ymax=5,
            alpha=1,
            fill="#70c18c")+ #classe 2
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=0,
            ymax=3,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "Demanda Bioquímica de Oxigênio no período 1990-2000",
        x="Estação",
        y="mg/L")+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_y_continuous(expand = expansion(mult = c(0.03,0.03)),
                      n.breaks = 8,
                      limits = c(1,100),
                      trans = "log10")+
   scale_x_discrete(limits = c("87398500", "87398980", "87398900",
                               "87398950", "87405500", "87406900", "87409900"))+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
 theme_grafs()
)
```

```{r Gráfico DBO período2, echo = FALSE, warning = FALSE, message = FALSE}
(dbo_p2<-ggplot(plan_wide_19902020 %>% 
                  filter(ANO_COLETA>"2000" &
                           ANO_COLETA<="2010"),
                aes(CODIGO,
                    DBO))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=10,
            ymax=Inf,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=5,
            ymax=10,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=3,
            ymax=5,
            alpha=1,
            fill="#70c18c")+ #classe 2
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=0,
            ymax=3,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "Demanda Bioquímica de Oxigênio no período 2000-2010",
        x="Estação",
        y="mg/L")+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_y_continuous(expand = expansion(mult = c(0.03,0.03)),
                      n.breaks = 8,
                      limits = c(1,100),
                      trans = "log10")+
   scale_x_discrete(limits = c("87398500", "87398980", "87398900", 
                               "87398950", "87405500", "87406900", "87409900"))+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
 theme_grafs()
)
```

```{r Gráfico DBO período3, echo = FALSE, warning = FALSE, message = FALSE}
(dbo_p3<-ggplot(plan_wide_19902020 %>% 
                  filter(ANO_COLETA>"2010" &
                           ANO_COLETA<="2020"),
                aes(CODIGO,
                    DBO, na.rm=TRUE))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=10,
            ymax=Inf,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=5,
            ymax=10,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=3,
            ymax=5,
            alpha=1,
            fill="#70c18c")+ #classe 2
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=0,
            ymax=3,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "Demanda Bioquímica de Oxigênio no período 2010-2020",
        x="Estação",
        y="mg/L")+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_y_continuous(expand = expansion(mult = c(0.03,0.03)),
                      n.breaks = 8,
                      limits = c(1,100),
                      trans = "log10")+
   scale_x_discrete(limits = c("87398500", "87398980", "87398900",
                               "87398950", "87405500", "87406900", "87409900"))+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
 theme_grafs()
)
```

```{r Gráfico IQA DBO periodo1, echo = FALSE, warning = FALSE, message = FALSE}
(iqa_dbo1<-ggplot(plan_wide_19902020 %>% 
                    filter(ANO_COLETA>"1990" &
                             ANO_COLETA<="2000"),
                  aes(CODIGO,
                      IQA_DBO))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=-Inf,
            ymax=19,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=19,
            ymax=36,
            alpha=1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=36,
            ymax=51,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=51,
            ymax=79,
            alpha=1,
            fill="#70c18c")+ #classe 2
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=79,
            ymax=Inf,
            alpha=1,
            fill="#8dcdeb")+ #classe 1))
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65),
                na.rm = TRUE)+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "Variação do IQA para o parâmetro DBO 1990-2020",
        x="Estação",
        y="mg/L")+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_y_continuous(expand = expansion(mult = c(0,0)),
                      n.breaks = 6,
                      limits = c(-1,101))+
   scale_x_discrete(limits = c("87398500", "87398980", "87398900", 
                               "87398950", "87405500", "87406900", "87409900"))+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
 theme_grafs()
)
```

```{r Gráfico IQA DBO periodo2, echo = FALSE, warning = FALSE, message = FALSE}
(iqa_dbo2<-ggplot(plan_wide_19902020%>% 
                    filter(ANO_COLETA>"2000" &
                             ANO_COLETA<="2010"),
                  aes(CODIGO,
                      IQA_DBO))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=-Inf,
            ymax=19,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=19,
            ymax=36,
            alpha=1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=36,
            ymax=51,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=51,
            ymax=79,
            alpha=1,
            fill="#70c18c")+ #classe 2
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=79,
            ymax=Inf,
            alpha=1,
            fill="#8dcdeb")+ #classe 1))
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65),
                na.rm = TRUE)+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "Variação do IQA para o parâmetro DBO 2000-2010",
        x="Estação",
        y="mg/L")+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_y_continuous(expand = expansion(mult = c(0,0)),
                      n.breaks = 6,
                      limits = c(-1,101))+
   scale_x_discrete(limits = c("87398500", "87398980", "87398900", "87398950",
                               "87405500", "87406900", "87409900"))+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
 theme_grafs()
 )
```

```{r Gráfico IQA DBO periodo3, echo = FALSE, warning = FALSE, message = FALSE}
(iqa_dbo3<-ggplot(plan_wide_19902020%>% 
                    filter(ANO_COLETA>"2010" &
                             ANO_COLETA<="2020"),
                  aes(CODIGO,
                      IQA_DBO))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=-Inf,
            ymax=19,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=19,
            ymax=36,
            alpha=1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=36,
            ymax=51,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=51,
            ymax=79,
            alpha=1,
            fill="#70c18c")+ #classe 2
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=79,
            ymax=Inf,
            alpha=1,
            fill="#8dcdeb")+ #classe 1))
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65),
                na.rm = TRUE)+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "Variação do IQA para o parâmetro DBO 2010-2020",
        x="Estação",
        y="mg/L")+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_y_continuous(expand = expansion(mult = c(0,0)),
                      n.breaks = 6,
                      limits = c(-1,101))+
   scale_x_discrete(limits = c("87398500", "87398980", "87398900", "87398950", "87405500", "87406900", "87409900"))+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
 theme_grafs()
 )
```

```{r Gráfico DBO 3 periodos juntos, warning=FALSE, message=FALSE}
grid.arrange(dbo_p1, dbo_p2, dbo_p3, ncol = 3)
```

```{r Sumário DBO}
(sum_dbo_p1 <- plan_wide_19902020 %>%
   select(CODIGO, DBO, ANO_COLETA) %>% 
   filter(ANO_COLETA>"1990" &
            ANO_COLETA<="2000") %>% 
   group_by(CODIGO) %>% 
   summarize(
     min = 
       min(DBO, 
           na.rm = TRUE),
     q1 = 
       quantile(DBO, 0.25, 
                na.rm = TRUE),
     median = 
       median(DBO, 
              na.rm = TRUE),
     mean = 
       mean(DBO, 
            na.rm= TRUE),
     q3 = 
       quantile(DBO, 0.75, 
                na.rm = TRUE),
     max = 
       max(DBO, 
           na.rm = TRUE))
)

(sum_dbo_p2 <- plan_wide_19902020 %>%
    select(CODIGO, DBO, ANO_COLETA) %>% 
    filter(ANO_COLETA>"2000" &
             ANO_COLETA<="2010") %>% 
    group_by(CODIGO) %>% 
    summarize(
      min = 
        min(DBO, 
            na.rm = TRUE),
      q1 = 
        quantile(DBO, 0.25, 
                 na.rm = TRUE),
      median = 
        median(DBO, 
               na.rm = TRUE),
      mean = 
        mean(DBO, 
             na.rm= TRUE),
      q3 = 
        quantile(DBO, 0.75, 
                 na.rm = TRUE),
      max = 
        max(DBO, 
            na.rm = TRUE))
)

(sum_dbo_p3 <- plan_wide_19902020 %>%
    select(CODIGO, DBO, ANO_COLETA) %>% 
    filter(ANO_COLETA>"2010" &
             ANO_COLETA<="2020") %>% 
    group_by(CODIGO) %>% 
    summarize(
      min = 
        min(DBO, 
            na.rm = TRUE),
      q1 = 
        quantile(DBO, 0.25, 
                 na.rm = TRUE),
      median = 
        median(DBO, 
               na.rm = TRUE),
      mean = 
        mean(DBO, 
             na.rm= TRUE),
      q3 = 
        quantile(DBO, 0.75, 
                 na.rm = TRUE),
      max = 
        max(DBO, 
            na.rm = TRUE))
)
```

```{r Salvando DBO}
ggsave("dbo_p1.png",
       plot = dbo_p1,
       path = "./graficos",
       dpi = 300,
       type = "cairo")


ggsave("dbo_p2.png",
       plot = dbo_p2,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("dbo_p3.png",
       plot = dbo_p3,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("dbo_3periodos.png",
       units = c("px"),
       width = 4500,
       height = 2993,
       plot = grid.arrange(dbo_p1, dbo_p2, dbo_p3, ncol = 3),
       path = "./graficos",
       dpi = 300,
       type = "cairo")
```
### Fósforo total

```{r Gráfico Fósforo total periodo1, warning = FALSE, message = FALSE}
(ptot_p1<-ggplot(plan_wide_19902020%>% 
                   filter(ANO_COLETA>"1990" &
                            ANO_COLETA<="2000"),
                 aes(CODIGO,
                     `Fósforo total`))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=0.15,
            ymax=Inf,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=0.1,
            ymax=0.15,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=0,
            ymax=0.1,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "Fósforo total no período 1990-2000",
        x="Estação",
        y="mg/L")+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_y_continuous(expand = expansion(mult = c(0.03,0.03)),
                      n.breaks = 8,
                      limits = c(min(plan_wide_19902020$`Fósforo total`, na.rm = TRUE),
                                 max(plan_wide_19902020$`Fósforo total`), na.rm = TRUE),
                      trans = "log10")+
   scale_x_discrete(limits = c("87398500", "87398980", "87398900", 
                               "87398950", "87405500", "87406900", "87409900"))+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
 theme_grafs()
)

```

```{r Gráfico Fósforo total periodo2, warning = FALSE, message = FALSE}
(ptot_p2 <- ggplot(plan_wide_19902020%>% 
                     filter(ANO_COLETA>"2000" &
                              ANO_COLETA<="2010"),
                   aes(CODIGO,
                       `Fósforo total`))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=0.15,
            ymax=Inf,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=0.1,
            ymax=0.15,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=0,
            ymax=0.1,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "Fósforo total no período 2000-2010",
        x="Estação",
        y="mg/L")+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_y_continuous(expand = expansion(mult = c(0.03,0.03)),
                      n.breaks = 8,
                      limits = c(min(plan_wide_19902020$`Fósforo total`, na.rm = TRUE),
                                 max(plan_wide_19902020$`Fósforo total`), na.rm = TRUE),
                      trans = "log10")+
   scale_x_discrete(limits = c("87398500", "87398980", "87398900", 
                               "87398950", "87405500", "87406900", "87409900"))+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
 theme_grafs()
)

```

```{r Gráfico Fósforo total periodo3, warning = FALSE, message = FALSE}
(ptot_p3 <- ggplot(plan_wide_19902020%>% 
                     filter(ANO_COLETA>"2010" &
                              ANO_COLETA<="2020"),
                   aes(CODIGO,
                       `Fósforo total`))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=0.15,
            ymax=Inf,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=0.1,
            ymax=0.15,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=0,
            ymax=0.1,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "Fósforo total no período 2010-2020",
        x="Estação",
        y="mg/L")+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_y_continuous(expand = expansion(mult = c(0.03,0.03)),
                      n.breaks = 8,
                      limits = c(min(plan_wide_19902020$`Fósforo total`, na.rm = TRUE),
                                 max(plan_wide_19902020$`Fósforo total`), na.rm = TRUE),
                      trans = "log10")+
   scale_x_discrete(limits = c("87398500", "87398980", "87398900", 
                               "87398950", "87405500", "87406900", "87409900"))+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
)

```

```{r Gráfico Ptot 3 periodos juntos, warning=FALSE, message=FALSE}
grid.arrange(ptot_p1, ptot_p2, ptot_p3, ncol = 3)
```

```{r Sumário Fósforo total}
(sum_ptot_p1 <- plan_wide_19902020 %>%
   select(CODIGO, `Fósforo total`, ANO_COLETA) %>% 
   filter(ANO_COLETA>"1990" &
            ANO_COLETA<="2000") %>% 
   group_by(CODIGO) %>% 
   summarize(
     min = 
       min(`Fósforo total`, na.rm = TRUE),
     q1 = 
       quantile(`Fósforo total`, 0.25, na.rm = TRUE),
     median = 
       median(`Fósforo total`, na.rm = TRUE),
     mean = 
       mean(`Fósforo total`, na.rm= TRUE),
     q3 = 
       quantile(`Fósforo total`, 0.75, na.rm = TRUE),
     max = 
       max(`Fósforo total`, na.rm = TRUE)))

(sum_ptot_p2 <- plan_wide_19902020 %>%
    select(CODIGO, `Fósforo total`, ANO_COLETA) %>% 
    filter(ANO_COLETA>"2000" &
             ANO_COLETA<="2010") %>% 
    group_by(CODIGO) %>% 
    summarize(
      min = 
        min(`Fósforo total`, na.rm = TRUE),
      q1 = 
        quantile(`Fósforo total`, 0.25, na.rm = TRUE),
      median = 
        median(`Fósforo total`, na.rm = TRUE),
      mean = 
        mean(`Fósforo total`, na.rm= TRUE),
      q3 = 
        quantile(`Fósforo total`, 0.75, na.rm = TRUE),
      max = 
        max(`Fósforo total`, na.rm = TRUE)))

(sum_ptot_p3 <- plan_wide_19902020 %>%
    select(CODIGO, `Fósforo total`, ANO_COLETA) %>% 
    filter(ANO_COLETA>"2010" &
             ANO_COLETA<="2020") %>% 
    group_by(CODIGO) %>% 
    summarize(
      min = 
        min(`Fósforo total`, na.rm = TRUE),
      q1 = 
        quantile(`Fósforo total`, 0.25, na.rm = TRUE),
      median = 
        median(`Fósforo total`, na.rm = TRUE),
      mean = 
        mean(`Fósforo total`, na.rm= TRUE),
      q3 = 
        quantile(`Fósforo total`, 0.75, na.rm = TRUE),
      max = 
        max(`Fósforo total`, na.rm = TRUE)))

```

```{r Salvando Ptot}
ggsave("ptot_p1.png",
       plot = ptot_p1,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("ptot_p2.png",
       plot = ptot_p2,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("ptot_p3.png",
       plot = ptot_p3,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("ptot_3periodos.png",
       units = c("px"),
       width = 4500,
       height = 2993,
       plot = grid.arrange(ptot_p1, ptot_p2, ptot_p3, ncol = 3),
       path = "./graficos",
       dpi = 300,
       type = "cairo")
```

### Escherichia coli

```{r Gráfico Ecoli periodo1, warning = FALSE, message = FALSE}
(ecoli_p1 <- ggplot(plan_wide_19902020 %>% 
                      filter(ANO_COLETA>"1990" &
                               ANO_COLETA<="2000"),
                    aes(CODIGO,
                        `Escherichia coli`))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=3200,
            ymax=Inf,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=800,
            ymax=3200,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=160,
            ymax=800,
            alpha=1,
            fill="#70c18c")+ #classe 2
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=0,
            ymax=160,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "Escherichia coli no período 1990-2000",
        x="Estação",
        y="NMP/100mL")+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_y_continuous(expand = expansion(mult = c(0.01, 0.01)),
                      n.breaks = 9,
                      limits = c(min(plan_wide_19902020$`Escherichia coli`, na.rm = TRUE),
                                 max(plan_wide_19902020$`Escherichia coli`, na.rm = TRUE)),
                      trans = "log10",
                      labels = scales::number_format(accuracy = 1,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
   scale_x_discrete(limits = c("87398500", "87398980", "87398900", 
                               "87398950", "87405500", "87406900", "87409900"))+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
)
```

```{r Gráfico Ecoli periodo2, warning = FALSE, message = FALSE}
(ecoli_p2 <- ggplot(plan_wide_19902020 %>% 
                      filter(ANO_COLETA>"2000" &
                               ANO_COLETA<="2010"),
                    aes(CODIGO,
                        `Escherichia coli`))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=3200,
            ymax=Inf,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=800,
            ymax=3200,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=160,
            ymax=800,
            alpha=1,
            fill="#70c18c")+ #classe 2
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=0,
            ymax=160,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "Escherichia coli no período 2000-2010",
        x="Estação",
        y="NMP/100mL")+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_y_continuous(expand = expansion(mult = c(0.01, 0.01)),
                      n.breaks = 9,
                      limits = c(min(plan_wide_19902020$`Escherichia coli`, na.rm = TRUE),
                                 max(plan_wide_19902020$`Escherichia coli`, na.rm = TRUE)),
                      trans = "log10",
                      labels = scales::number_format(accuracy = 1,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
   scale_x_discrete(limits = c("87398500", "87398980", "87398900", 
                               "87398950", "87405500", "87406900", "87409900"))+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
)
```

```{r Gráfico Ecoli periodo3, warning = FALSE, message = FALSE}
(ecoli_p3 <- ggplot(plan_wide_19902020 %>% 
                      filter(ANO_COLETA>"2010" &
                               ANO_COLETA<="2020"),
                    aes(CODIGO,
                        `Escherichia coli`))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=3200,
            ymax=Inf,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=800,
            ymax=3200,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=160,
            ymax=800,
            alpha=1,
            fill="#70c18c")+ #classe 2
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=0,
            ymax=160,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "Escherichia coli no período 2010-2020",
        x="Estação",
        y="NMP/100mL")+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_y_continuous(expand = expansion(mult = c(0.01, 0.01)),
                      n.breaks = 9,
                      limits = c(min(plan_wide_19902020$`Escherichia coli`, na.rm = TRUE),
                                 max(plan_wide_19902020$`Escherichia coli`, na.rm = TRUE)),
                      trans = "log10",
                      labels = scales::number_format(accuracy = 1,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
   scale_x_discrete(limits = c("87398500", "87398980", "87398900", 
                               "87398950", "87405500", "87406900", "87409900"))+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
)
```

```{r Gráfico ecoli 3 periodos juntos, warning=FALSE, message=FALSE}
grid.arrange(ecoli_p1, ecoli_p2, ecoli_p3, ncol = 3)
```

```{r Sumário Ecoli}
(sum_ecoli_p1 <- plan_wide_19902020 %>%
   select(CODIGO, `Escherichia coli`, ANO_COLETA) %>% 
   filter(ANO_COLETA>"1990" &
            ANO_COLETA<="2000") %>% 
   group_by(CODIGO) %>% 
   summarize(
     min = 
       min(`Escherichia coli`, 
           na.rm = TRUE),
     q1 = 
       quantile(`Escherichia coli`, 0.25, 
                na.rm = TRUE),
     median = 
       median(`Escherichia coli`, 
              na.rm = TRUE),
     mean = 
       mean(`Escherichia coli`, 
            na.rm= TRUE),
     q3 = 
       quantile(`Escherichia coli`, 0.75, 
                na.rm = TRUE),
     max = 
       max(`Escherichia coli`, 
           na.rm = TRUE))
)

(sum_ecoli_p2 <- plan_wide_19902020 %>%
    select(CODIGO, `Escherichia coli`, ANO_COLETA) %>% 
    filter(ANO_COLETA>"2000" &
             ANO_COLETA<="2010") %>% 
    group_by(CODIGO) %>% 
    summarize(
      min = 
        min(`Escherichia coli`, 
            na.rm = TRUE),
      q1 = 
        quantile(`Escherichia coli`, 0.25, 
                 na.rm = TRUE),
      median = 
        median(`Escherichia coli`, 
               na.rm = TRUE),
      mean = 
        mean(`Escherichia coli`, 
             na.rm= TRUE),
      q3 = 
        quantile(`Escherichia coli`, 0.75, 
                 na.rm = TRUE),
      max = 
        max(`Escherichia coli`, 
            na.rm = TRUE))
)

(sum_ecoli_p3 <- plan_wide_19902020 %>%
    select(CODIGO, `Escherichia coli`, ANO_COLETA) %>% 
    filter(ANO_COLETA>"2010" &
             ANO_COLETA<="2020") %>% 
    group_by(CODIGO) %>% 
    summarize(
      min = 
        min(`Escherichia coli`, 
            na.rm = TRUE),
      q1 = 
        quantile(`Escherichia coli`, 0.25, 
                 na.rm = TRUE),
      median = 
        median(`Escherichia coli`, 
               na.rm = TRUE),
      mean = 
        mean(`Escherichia coli`, 
             na.rm= TRUE),
      q3 = 
        quantile(`Escherichia coli`, 0.75, 
                 na.rm = TRUE),
      max = 
        max(`Escherichia coli`, 
            na.rm = TRUE))
)
```

```{r Salvando ecoli}
ggsave("ecoli_p1.png",
       plot = ecoli_p1,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("ecoli_p2.png",
       plot = ecoli_p2,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("ecoli_p3.png",
       plot = ecoli_p3,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("ecoli_3periodos.png",
       units = c("px"),
       width = 4500,
       height = 2993,
       plot = grid.arrange(ecoli_p1, ecoli_p2, ecoli_p3, ncol = 3),
       path = "./graficos",
       dpi = 300,
       type = "cairo")
```
### Nitrogênio amoniacal

```{r Gráfico Nitrogênio total periodo1, warning = FALSE, message = FALSE}
(namon_p1 <- ggplot(plan_wide_19902020 %>% 
                      filter(ANO_COLETA>"1990" &
                               ANO_COLETA<="2000"),
                    aes(CODIGO,
                        `Nitrogênio total`))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=13.3,
            ymax=Inf,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=3.7,
            ymax=13.3,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=0,
            ymax=3.7,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "Nitrogênio amoniacal no período 1990-2000",
        x="Estação",
        y="mg/L")+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_y_continuous(expand = expansion(mult = c(0.01, 0.05)),
                      n.breaks = 9,
                      limits = c(min(plan_wide_19902020$`Nitrogênio total`, na.rm = TRUE),
                                 max(plan_wide_19902020$`Nitrogênio total`, na.rm = TRUE)),
                      trans = "log10",
                      labels = scales::number_format(accuracy = .001,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
   scale_x_discrete(limits = c("87398500", "87398980", "87398900", 
                               "87398950", "87405500", "87406900", "87409900"))+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
)
```

```{r Gráfico Nitrogênio total periodo2, warning = FALSE, message = FALSE}
(namon_p2 <- ggplot(plan_wide_19902020 %>% 
                      filter(ANO_COLETA>"2000" &
                               ANO_COLETA<="2010"),
                    aes(CODIGO,
                        `Nitrogênio total`))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=13.3,
            ymax=Inf,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=3.7,
            ymax=13.3,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=0,
            ymax=3.7,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "Nitrogênio amoniacal no período 2000-2010",
        x="Estação",
        y="mg/L")+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_y_continuous(expand = expansion(mult = c(0.01, 0.05)),
                      n.breaks = 9,
                      limits = c(min(plan_wide_19902020$`Nitrogênio total`, na.rm = TRUE),
                                 max(plan_wide_19902020$`Nitrogênio total`, na.rm = TRUE)),
                      trans = "log10",
                      labels = scales::number_format(accuracy = .001,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
   scale_x_discrete(limits = c("87398500", "87398980", "87398900", 
                               "87398950", "87405500", "87406900", "87409900"))+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
)
```

```{r Gráfico Nitrogênio total periodo3, warning = FALSE, message = FALSE}
(namon_p3 <- ggplot(plan_wide_19902020 %>% 
                      filter(ANO_COLETA>"2010" &
                               ANO_COLETA<="2020"),
                    aes(CODIGO,
                        `Nitrogênio total`))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=13.3,
            ymax=Inf,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=3.7,
            ymax=13.3,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=0,
            ymax=3.7,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "Nitrogênio amoniacal no período 2010-2020",
        x="Estação",
        y="mg/L")+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_y_continuous(expand = expansion(mult = c(0.01, 0.05)),
                      n.breaks = 9,
                      limits = c(min(plan_wide_19902020$`Nitrogênio total`, na.rm = TRUE),
                                 max(plan_wide_19902020$`Nitrogênio total`, na.rm = TRUE)),
                      trans = "log10",
                      labels = scales::number_format(accuracy = .001,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
   scale_x_discrete(limits = c("87398500", "87398980", "87398900", 
                               "87398950", "87405500", "87406900", "87409900"))+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
)
```

```{r Gráfico Namon 3 periodos juntos, warning=FALSE, message=FALSE}
grid.arrange(namon_p1, namon_p2, namon_p3, ncol = 3)
```

```{r Sumário Nitrogênio total}
(sum_namon_p1 <- plan_wide_19902020 %>%
   select(CODIGO, `Nitrogênio total`, ANO_COLETA) %>% 
   filter(ANO_COLETA>"1990" &
            ANO_COLETA<="2000") %>% 
   group_by(CODIGO) %>% 
   summarize(
     min = 
       min(`Nitrogênio total`, 
           na.rm = TRUE),
     q1 = 
       quantile(`Nitrogênio total`, 0.25, 
                na.rm = TRUE),
     median = 
       median(`Nitrogênio total`, 
              na.rm = TRUE),
     mean = 
       mean(`Nitrogênio total`, 
            na.rm= TRUE),
     q3 = 
       quantile(`Nitrogênio total`, 0.75, 
                na.rm = TRUE),
     max = 
       max(`Nitrogênio total`, 
           na.rm = TRUE))
)

(sum_namon_p2 <- plan_wide_19902020 %>%
    select(CODIGO, `Nitrogênio total`, ANO_COLETA) %>% 
    filter(ANO_COLETA>"2000" &
             ANO_COLETA<="2010") %>% 
    group_by(CODIGO) %>% 
    summarize(
      min = 
        min(`Nitrogênio total`, 
            na.rm = TRUE),
      q1 = 
        quantile(`Nitrogênio total`, 0.25, 
                 na.rm = TRUE),
      median = 
        median(`Nitrogênio total`, 
               na.rm = TRUE),
      mean = 
        mean(`Nitrogênio total`, 
             na.rm= TRUE),
      q3 = 
        quantile(`Nitrogênio total`, 0.75, 
                 na.rm = TRUE),
      max = 
        max(`Nitrogênio total`, 
            na.rm = TRUE))
)

(sum_namon_p3 <- plan_wide_19902020 %>%
    select(CODIGO, `Nitrogênio total`, ANO_COLETA) %>% 
    filter(ANO_COLETA>"2010" &
             ANO_COLETA<="2020") %>% 
    group_by(CODIGO) %>% 
    summarize(
      min = 
        min(`Nitrogênio total`, 
            na.rm = TRUE),
      q1 = 
        quantile(`Nitrogênio total`, 0.25, 
                 na.rm = TRUE),
      median = 
        median(`Nitrogênio total`, 
               na.rm = TRUE),
      mean = 
        mean(`Nitrogênio total`, 
             na.rm= TRUE),
      q3 = 
        quantile(`Nitrogênio total`, 0.75, 
                 na.rm = TRUE),
      max = 
        max(`Nitrogênio total`, 
            na.rm = TRUE))
)
```

```{r Salvando namon}
ggsave("namon_p1.png",
       plot = namon_p1,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("namon_p2.png",
       plot = namon_p2,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("namon_p3.png",
       plot = namon_p3,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("namon_3periodos.png",
       units = c("px"),
       width = 4500,
       height = 2993,
       plot = grid.arrange(namon_p1, namon_p2, namon_p3, ncol = 3),
       path = "./graficos",
       dpi = 300,
       type = "cairo")
```

### Turbidez
```{r Gráfico Turbidez periodo1, warning = FALSE, message = FALSE}
(turb_p1 <- ggplot(plan_wide_19902020 %>% 
                     filter(ANO_COLETA>"1990" &
                              ANO_COLETA<="2000"),
                   aes(CODIGO,
                       Turbidez))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=100,
            ymax=Inf,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=40,
            ymax=100,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=0,
            ymax=40,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "Turbidez no período 1990-2000",
        x="Estação",
        y="UNT")+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_y_continuous(expand = expansion(mult = c(0.05, 0.03)),
                      n.breaks = 8,
                      limits = c(min(plan_wide_19902020$Turbidez, na.rm = TRUE),
                                 max(plan_wide_19902020$Turbidez, na.rm = TRUE)),
                      trans = "log10",
                      labels = scales::number_format(accuracy = 1,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
   scale_x_discrete(limits = c("87398500", "87398980", "87398900",
                               "87398950", "87405500", "87406900", "87409900"))+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
)
```

```{r Gráfico Turbidez periodo2, warning = FALSE, message = FALSE}
(turb_p2 <- ggplot(plan_wide_19902020 %>% 
                     filter(ANO_COLETA>"2000" &
                              ANO_COLETA<="2010"),
                   aes(CODIGO,
                       Turbidez))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=100,
            ymax=Inf,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=40,
            ymax=100,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=0,
            ymax=40,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "Turbidez no período 2000-2010",
        x="Estação",
        y="UNT")+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_y_continuous(expand = expansion(mult = c(0.05, 0.03)),
                      n.breaks = 8,
                      limits = c(min(plan_wide_19902020$Turbidez, na.rm = TRUE),
                                 max(plan_wide_19902020$Turbidez, na.rm = TRUE)),
                      trans = "log10",
                      labels = scales::number_format(accuracy = 1,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
   scale_x_discrete(limits = c("87398500", "87398980", "87398900", 
                               "87398950", "87405500", "87406900", "87409900"))+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
)
```

```{r Gráfico Turbidez periodo3, warning = FALSE, message = FALSE}
(turb_p3 <- ggplot(plan_wide_19902020 %>% 
                     filter(ANO_COLETA>"2010" &
                              ANO_COLETA<="2020"),
                   aes(CODIGO,
                       Turbidez))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=100,
            ymax=Inf,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=40,
            ymax=100,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=0,
            ymax=40,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "Turbidez no período 2010-2020",
        x="Estação",
        y="UNT")+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_y_continuous(expand = expansion(mult = c(0.05, 0.03)),
                      n.breaks = 8,
                      limits = c(min(plan_wide_19902020$Turbidez, na.rm = TRUE),
                                 max(plan_wide_19902020$Turbidez, na.rm = TRUE)),
                      trans = "log10",
                      labels = scales::number_format(accuracy = 1,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
   scale_x_discrete(limits = c("87398500", "87398980", "87398900", 
                               "87398950", "87405500", "87406900", "87409900"))+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
)
```

```{r Gráfico turb 3 periodos juntos, warning=FALSE, message=FALSE}
grid.arrange(turb_p1, turb_p2, turb_p3, ncol = 3)
```

```{r Sumário Turbidez}
(sum_turb_p1 <- plan_wide_19902020 %>%
   select(CODIGO, Turbidez, ANO_COLETA) %>% 
   filter(ANO_COLETA>"1990" &
            ANO_COLETA<="2000") %>% 
   group_by(CODIGO) %>% 
   summarize(
     min = 
       min(Turbidez, 
           na.rm = TRUE),
     q1 = 
       quantile(Turbidez, 0.25, 
                na.rm = TRUE),
     median = 
       median(Turbidez, 
              na.rm = TRUE),
     mean = 
       mean(Turbidez, 
            na.rm= TRUE),
     q3 = 
       quantile(Turbidez, 0.75, 
                na.rm = TRUE),
     max = 
       max(Turbidez, 
           na.rm = TRUE))
)

(sum_turb_p2 <- plan_wide_19902020 %>%
    select(CODIGO, Turbidez, ANO_COLETA) %>% 
    filter(ANO_COLETA>"2000" &
             ANO_COLETA<="2010") %>% 
    group_by(CODIGO) %>% 
    summarize(
      min = 
        min(Turbidez, 
            na.rm = TRUE),
      q1 = 
        quantile(Turbidez, 0.25, 
                 na.rm = TRUE),
      median = 
        median(Turbidez, 
               na.rm = TRUE),
      mean = 
        mean(Turbidez, 
             na.rm= TRUE),
      q3 = 
        quantile(Turbidez, 0.75, 
                 na.rm = TRUE),
      max = 
        max(Turbidez, 
            na.rm = TRUE))
)

(sum_turb_p3 <- plan_wide_19902020 %>%
    select(CODIGO, Turbidez, ANO_COLETA) %>% 
    filter(ANO_COLETA>"2010" &
             ANO_COLETA<="2020") %>% 
    group_by(CODIGO) %>% 
    summarize(
      min = 
        min(Turbidez, 
            na.rm = TRUE),
      q1 = 
        quantile(Turbidez, 0.25, 
                 na.rm = TRUE),
      median = 
        median(Turbidez, 
               na.rm = TRUE),
      mean = 
        mean(Turbidez, 
             na.rm= TRUE),
      q3 = 
        quantile(Turbidez, 0.75, 
                 na.rm = TRUE),
      max = 
        max(Turbidez, 
            na.rm = TRUE))
) 
```

```{r Salvando turb}
ggsave("turb_p1.png",
       plot = turb_p1,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("turb_p2.png",
       plot = turb_p2,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("turb_p3.png",
       plot = turb_p3,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("turb_3periodos.png",
       units = c("px"),
       width = 4500,
       height = 2993,
       plot = grid.arrange(turb_p1, turb_p2, turb_p3, ncol = 3),
       path = "./graficos",
       dpi = 300,
       type = "cairo")
```

### pH
```{r Gráfico pH periodo1, warning = FALSE, message = FALSE}
(pH_p1 <- ggplot(plan_wide_19902020 %>% 
                   filter(ANO_COLETA>"1990" &
                            ANO_COLETA<="2000"),
                 aes(CODIGO,
                     pH))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=-Inf,
            ymax=6,
            alpha=1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=9,
            ymax=Inf,
            alpha=1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=6,
            ymax=9,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "pH no período 1990-2000",
        x="Estação",
        y="")+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_y_continuous(expand = expansion(mult = c(0.01, 0.01)),
                      n.breaks = 8,
                      limits = c(4,11),
                      labels = scales::number_format(accuracy = 1,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
   scale_x_discrete(limits = c("87398500", "87398980", "87398900", 
                               "87398950", "87405500", "87406900", "87409900"))+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
)
```

```{r Gráfico pH periodo2, warning = FALSE, message = FALSE}
(pH_p2 <- ggplot(plan_wide_19902020 %>% 
                   filter(ANO_COLETA>"2000" &
                            ANO_COLETA<="2010"),
                 aes(CODIGO,
                     pH))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=-Inf,
            ymax=6,
            alpha=1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=9,
            ymax=Inf,
            alpha=1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=6,
            ymax=9,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "pH no período 2000-2010",
        x="Estação",
        y="")+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_y_continuous(expand = expansion(mult = c(0.01, 0.01)),
                      n.breaks = 8,
                      limits = c(4,11),
                      labels = scales::number_format(accuracy = 1,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
   scale_x_discrete(limits = c("87398500", "87398980", "87398900",
                               "87398950", "87405500", "87406900", "87409900"))+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
)
```

```{r Gráfico pH periodo3, warning = FALSE, message = FALSE}
(pH_p3 <- ggplot(plan_wide_19902020 %>% 
                   filter(ANO_COLETA>"2010" &
                            ANO_COLETA<="2020"),
                 aes(CODIGO,
                     pH))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=-Inf,
            ymax=6,
            alpha=1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=9,
            ymax=Inf,
            alpha=1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=6,
            ymax=9,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "pH no período 2010-2020",
        x="Estação",
        y="")+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_y_continuous(expand = expansion(mult = c(0.01, 0.01)),
                      n.breaks = 8,
                      limits = c(4,11),
                      labels = scales::number_format(accuracy = 1,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
   scale_x_discrete(limits = c("87398500", "87398980", "87398900",
                               "87398950", "87405500", "87406900", "87409900"))+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
)
```

```{r Gráfico pH 3 periodos juntos, warning=FALSE, message=FALSE}
grid.arrange(pH_p1, pH_p2, pH_p3, ncol = 3)
```

```{r Sumário pH}
(sum_pH_p1 <- plan_wide_19902020 %>%
   select(CODIGO, pH, ANO_COLETA) %>% 
   filter(ANO_COLETA>"1990" &
            ANO_COLETA<="2000") %>% 
   group_by(CODIGO) %>% 
   summarize(
     min = 
       min(pH, 
           na.rm = TRUE),
     q1 = 
       quantile(pH, 0.25, 
                na.rm = TRUE),
     median = 
       median(pH, 
              na.rm = TRUE),
     mean = 
       mean(pH, 
            na.rm= TRUE),
     q3 = 
       quantile(pH, 0.75, 
                na.rm = TRUE),
     max = 
       max(pH, 
           na.rm = TRUE))
)

(sum_pH_p2 <- plan_wide_19902020 %>%
    select(CODIGO, pH, ANO_COLETA) %>% 
    filter(ANO_COLETA>"2000" &
             ANO_COLETA<="2010") %>% 
    group_by(CODIGO) %>% 
    summarize(
      min = 
        min(pH, 
            na.rm = TRUE),
      q1 = 
        quantile(pH, 0.25, 
                 na.rm = TRUE),
      median = 
        median(pH, 
               na.rm = TRUE),
      mean = 
        mean(pH, 
             na.rm= TRUE),
      q3 = 
        quantile(pH, 0.75, 
                 na.rm = TRUE),
      max = 
        max(pH, 
            na.rm = TRUE))
) 

(sum_pH_p3 <- plan_wide_19902020 %>%
    select(CODIGO, pH, ANO_COLETA) %>% 
    filter(ANO_COLETA>"2010" &
             ANO_COLETA<="2020") %>% 
    group_by(CODIGO) %>% 
    summarize(
      min = 
        min(pH, 
            na.rm = TRUE),
      q1 = 
        quantile(pH, 0.25, 
                 na.rm = TRUE),
      median = 
        median(pH, 
               na.rm = TRUE),
      mean = 
        mean(pH, 
             na.rm= TRUE),
      q3 = 
        quantile(pH, 0.75, 
                 na.rm = TRUE),
      max = 
        max(pH, 
            na.rm = TRUE))
)
```

```{r Salvando pH}
ggsave("pH_p1.png",
       plot = pH_p1,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("pH_p2.png",
       plot = pH_p2,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("pH_p3.png",
       plot = pH_p3,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("pH_3periodos.png",
       units = c("px"),
       width = 4500,
       height = 2993,
       plot = grid.arrange(pH_p1, pH_p2, pH_p3, ncol = 3),
       path = "./graficos",
       dpi = 300,
       type = "cairo")
```

### Sólidos totais

```{r Gráfico SólTot periodo1, warning = FALSE, message = FALSE}
(SolTot_p1 <- ggplot(plan_wide_19902020 %>% 
                       filter(ANO_COLETA>"1990" &
                                ANO_COLETA<="2000"),
                     aes(CODIGO,
                         `Sólidos totais`))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=500,
            ymax=Inf,
            alpha=1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=-Inf,
            ymax=500,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "Sólidos totais no período 1990-2000",
        x="Estação",
        y="")+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_y_continuous(expand = expansion(mult = c(0.01, 0.05)),
                      n.breaks = 8,
                      limits = c(0,
                                 max(plan_wide_19902020$`Sólidos totais`, na.rm = TRUE)),
                      labels = scales::number_format(accuracy = 1,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
   scale_x_discrete(limits = c("87398500", "87398980", "87398900", 
                               "87398950", "87405500", "87406900", "87409900"))+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
)
```

```{r Gráfico SólTot periodo2, warning = FALSE, message = FALSE}
(SolTot_p2 <- ggplot(plan_wide_19902020 %>% 
                       filter(ANO_COLETA>"2000" &
                                ANO_COLETA<="2010"),
                     aes(CODIGO,
                         `Sólidos totais`))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=500,
            ymax=Inf,
            alpha=1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=-Inf,
            ymax=500,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "Sólidos totais no período 2000-2010",
        x="Estação",
        y="")+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_y_continuous(expand = expansion(mult = c(0.01, 0.05)),
                      n.breaks = 8,
                      limits = c(0,
                                 max(plan_wide_19902020$`Sólidos totais`, na.rm = TRUE)),
                      labels = scales::number_format(accuracy = 1,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
   scale_x_discrete(limits = c("87398500", "87398980", "87398900",
                               "87398950", "87405500", "87406900", "87409900"))+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
)
```

```{r Gráfico SólTot periodo3, warning = FALSE, message = FALSE}
(SolTot_p3 <- ggplot(plan_wide_19902020 %>% 
                       filter(ANO_COLETA>"2010" &
                                ANO_COLETA<="2020"),
                     aes(CODIGO,
                         `Sólidos totais`))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=500,
            ymax=Inf,
            alpha=1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=-Inf,
            ymax=500,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "Sólidos totais no período 2010-2020",
        x="Estação",
        y="")+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_y_continuous(expand = expansion(mult = c(0.01, 0.05)),
                      n.breaks = 8,
                      limits = c(0,
                                 max(plan_wide_19902020$`Sólidos totais`, na.rm = TRUE)),
                      labels = scales::number_format(accuracy = 1,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
   scale_x_discrete(limits = c("87398500", "87398980", "87398900",
                               "87398950", "87405500", "87406900", "87409900"))+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme_grafs()
)
```

```{r Gráfico SólTot 3 periodos juntos, warning=FALSE, message=FALSE}
grid.arrange(SolTot_p1, SolTot_p2, SolTot_p3, ncol = 3)
```

```{r Sumário Sólidos Totais}
(sum_SolTot_p1 <- plan_wide_19902020 %>%
   select(CODIGO, `Sólidos totais`, ANO_COLETA) %>% 
   filter(ANO_COLETA>"1990" &
            ANO_COLETA<="2000") %>% 
   group_by(CODIGO) %>% 
   summarize(
     min = 
       min(`Sólidos totais`, 
           na.rm = TRUE),
     q1 = 
       quantile(`Sólidos totais`, 0.25, 
                na.rm = TRUE),
     median = 
       median(`Sólidos totais`, 
              na.rm = TRUE),
     mean = 
       mean(`Sólidos totais`, 
            na.rm= TRUE),
     q3 = 
       quantile(`Sólidos totais`, 0.75, 
                na.rm = TRUE),
     max = 
       max(`Sólidos totais`, 
           na.rm = TRUE))
)

(sum_SolTot_p2 <- plan_wide_19902020 %>%
    select(CODIGO, `Sólidos totais`, ANO_COLETA) %>% 
    filter(ANO_COLETA>"2000" &
             ANO_COLETA<="2010") %>% 
    group_by(CODIGO) %>% 
    summarize(
      min = 
        min(`Sólidos totais`, 
            na.rm = TRUE),
      q1 = 
        quantile(`Sólidos totais`, 0.25, 
                 na.rm = TRUE),
      median = 
        median(`Sólidos totais`, 
               na.rm = TRUE),
      mean = 
        mean(`Sólidos totais`, 
             na.rm= TRUE),
      q3 = 
        quantile(`Sólidos totais`, 0.75, 
                 na.rm = TRUE),
      max = 
        max(`Sólidos totais`, 
            na.rm = TRUE))
)

(sum_SolTot_p3 <- plan_wide_19902020 %>%
    select(CODIGO, `Sólidos totais`, ANO_COLETA) %>% 
    filter(ANO_COLETA>"2010" &
             ANO_COLETA<="2020") %>% 
    group_by(CODIGO) %>% 
    summarize(
      min = 
        min(`Sólidos totais`, 
            na.rm = TRUE),
      q1 = 
        quantile(`Sólidos totais`, 0.25, 
                 na.rm = TRUE),
      median = 
        median(`Sólidos totais`, 
               na.rm = TRUE),
      mean = 
        mean(`Sólidos totais`, 
             na.rm= TRUE),
      q3 = 
        quantile(`Sólidos totais`, 0.75, 
                 na.rm = TRUE),
      max = 
        max(`Sólidos totais`, 
            na.rm = TRUE))
)
```

```{r Salvando SolTot}
ggsave("SolTot_p1.png",
       plot = SolTot_p1,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("SolTot_p2.png",
       plot = SolTot_p2,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("SolTot_p3.png",
       plot = SolTot_p3,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("SolTot_3periodos.png",
       units = c("px"),
       width = 4500,
       height = 2993,
       plot = grid.arrange(SolTot_p1, SolTot_p2, SolTot_p3, ncol = 3),
       path = "./graficos",
       dpi = 300,
       type = "cairo")
```

### IQA

```{r Gráfico IQA periodo1, echo = FALSE, message=FALSE, warning=FALSE}
(iqa_p1 <-ggplot(plan_wide_19902020 %>% 
                   filter(ANO_COLETA > "1990" &
                            ANO_COLETA <= "2000"),
                 aes(CODIGO,
                     IQA, na.rm = TRUE))+
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=-Inf,
             ymax=19,
             alpha=1,
             fill="#ac5079")+ #>pior classe
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=19,
             ymax=36,
             alpha=1,
             fill="#eb5661")+ #classe 4
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=36,
             ymax=51,
             alpha=1,
             fill="#fcf7ab")+ #classe 3
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=51,
             ymax=79,
             alpha=1,
             fill="#70c18c")+ #classe 2
    annotate("rect",
             xmin=-Inf,
             xmax=Inf,
             ymin=79,
             ymax=Inf,
             alpha=1,
             fill="#8dcdeb")+ #classe 1
    stat_boxplot(geom = 'errorbar',
                 width=0.3,
                 position = position_dodge(width = 0.65),
                 na.rm = TRUE)+
    geom_boxplot(fill='#F8F8FF',
                 color="black",
                 outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
                 width= 0.7,
                 na.rm = TRUE)+
    labs(title = "Variação do IQA no período 1990-2000",
         x="Estação",
         y="")+
    ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
    scale_y_continuous(expand = expansion(mult = c(0,0)),
                       n.breaks = 6,
                       limits = c(-1,101))+
    scale_x_discrete(limits = c("87398500", "87398980", "87398900",
                                "87398950", "87405500", "87406900", "87409900"))+
    geom_smooth(method = "lm",
                se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
                aes(group=1),
                alpha=.5,
                na.rm = TRUE,
                size = 1)+
   theme_grafs()+
   theme(axis.title.y = element_blank())
)
```

```{r Gráfico IQA periodo2, echo = FALSE, message=FALSE, warning=FALSE}
(iqa_p2 <-ggplot(plan_wide_19902020 %>% 
                   filter(ANO_COLETA > "2000" &
                            ANO_COLETA <= "2010"),
                 aes(CODIGO,
                     IQA, na.rm = TRUE))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=-Inf,
            ymax=19,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=19,
            ymax=36,
            alpha=1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=36,
            ymax=51,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=51,
            ymax=79,
            alpha=1,
            fill="#70c18c")+ #classe 2
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=79,
            ymax=Inf,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65),
                na.rm = TRUE)+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7,
                na.rm = TRUE)+
   labs(title = "Variação do IQA no período 2000-2010",
        x="Estação",
        y="")+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_y_continuous(expand = expansion(mult = c(0,0)),
                      n.breaks = 6,
                      limits = c(-1,101))+
   scale_x_discrete(limits = c("87398500", "87398980", "87398900",
                               "87398950", "87405500", "87406900", "87409900"))+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
 theme_grafs()+
   theme(axis.title.y = element_blank()
   )
)
```

```{r Gráfico IQA periodo3, echo = FALSE, message=FALSE, warning=FALSE}
(iqa_p3 <-ggplot(plan_wide_19902020 %>% 
                   filter(ANO_COLETA > "2010" &
                            ANO_COLETA <= "2020"),
                 aes(CODIGO,
                     IQA, na.rm = TRUE))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=-Inf,
            ymax=19,
            alpha=1,
            fill="#ac5079")+ #>pior classe
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=19,
            ymax=36,
            alpha=1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=36,
            ymax=51,
            alpha=1,
            fill="#fcf7ab")+ #classe 3
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=51,
            ymax=79,
            alpha=1,
            fill="#70c18c")+ #classe 2
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=79,
            ymax=Inf,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65),
                na.rm = TRUE)+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7,
                na.rm = TRUE)+
   labs(title = "Variação do IQA no período 2010-2020",
        x="Estação",
        y="")+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_y_continuous(expand = expansion(mult = c(0,0)),
                      n.breaks = 6,
                      limits = c(-1,101))+
   scale_x_discrete(limits = c("87398500", "87398980", "87398900",
                               "87398950", "87405500", "87406900", "87409900"))+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
 theme_grafs()+
   theme(axis.title.y = element_blank())
 )
```

```{r Gráfico IQA 3 periodos juntos, warning=FALSE, message=FALSE}
grid.arrange(iqa_p1, iqa_p2, iqa_p3, ncol = 3)
```

```{r Sumário IQA}
(sum_IQA_p1 <- plan_wide_19902020 %>%
   select(CODIGO, IQA, ANO_COLETA) %>% 
   filter(ANO_COLETA>"1990" &
            ANO_COLETA<="2000") %>% 
   group_by(CODIGO) %>% 
   summarize(
     min = 
       min(IQA, 
           na.rm = TRUE),
     q1 = 
       quantile(IQA, 0.25, 
                na.rm = TRUE),
     median = 
       median(IQA, 
              na.rm = TRUE),
     mean = 
       mean(IQA, 
            na.rm= TRUE),
     q3 = 
       quantile(IQA, 0.75, 
                na.rm = TRUE),
     max = 
       max(IQA, 
           na.rm = TRUE))
)

(sum_IQA_p2 <- plan_wide_19902020 %>%
    select(CODIGO, IQA, ANO_COLETA) %>% 
    filter(ANO_COLETA>"2000" &
             ANO_COLETA<="2010") %>% 
    group_by(CODIGO) %>% 
    summarize(
      min = 
        min(IQA, 
            na.rm = TRUE),
      q1 = 
        quantile(IQA, 0.25, 
                 na.rm = TRUE),
      median = 
        median(IQA, 
               na.rm = TRUE),
      mean = 
        mean(IQA, 
             na.rm= TRUE),
      q3 = 
        quantile(IQA, 0.75, 
                 na.rm = TRUE),
      max = 
        max(IQA, 
            na.rm = TRUE))
)

(sum_IQA_p3 <- plan_wide_19902020 %>%
    select(CODIGO, IQA, ANO_COLETA) %>% 
    filter(ANO_COLETA>"2010" &
             ANO_COLETA<="2020") %>% 
    group_by(CODIGO) %>% 
    summarize(
      min = 
        min(IQA, 
            na.rm = TRUE),
      q1 = 
        quantile(IQA, 0.25, 
                 na.rm = TRUE),
      median = 
        median(IQA, 
               na.rm = TRUE),
      mean = 
        mean(IQA, 
             na.rm= TRUE),
      q3 = 
        quantile(IQA, 0.75, 
                 na.rm = TRUE),
      max = 
        max(IQA, 
            na.rm = TRUE),
      n = 
        length(IQA))
)

plan_wide_19902020 %>% 
  select(CODIGO, IQA) %>% 
  group_by(CODIGO) %>% 
  summarize(
    min = 
      min(IQA, 
          na.rm = TRUE),
    q1 = 
      quantile(IQA, 0.25, 
               na.rm = TRUE),
    median = 
      median(IQA, 
             na.rm = TRUE),
    mean = 
      mean(IQA, 
           na.rm= TRUE),
    q3 = 
      quantile(IQA, 0.75, 
               na.rm = TRUE),
    max = 
      max(IQA, 
          na.rm = TRUE))
```

```{r Salvando iqa}
ggsave("iqa_p1.png",
       plot = iqa_p1,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("iqa_p2.png",
       plot = iqa_p2,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("iqa_p3.png",
       plot = iqa_p3,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("iqa_3periodos.png",
       units = c("px"),
       width = 4500,
       height = 2993,
       plot = grid.arrange(iqa_p1, iqa_p2, iqa_p3, ncol = 3),
       path = "./graficos",
       dpi = 300,
       type = "cairo")
```

## Testando coisas
```{r Testando coisas, include = FALSE}
# plan_wide_19902020 %>% 
#    select(CODIGO, `Oxigênio dissolvido`, ANO_COLETA) %>% 
#    ggplot(aes(ANO_COLETA, `Oxigênio dissolvido`, 
#       col = CODIGO))+
#    geom_line()+
#    facet_wrap(~ CODIGO, ncol = 7)

# df111 <- data.frame(x = c(1:100))
# glimpse(df111)
# df111$y <- 2 + 3 * df111$x + rnorm(100, sd = 40)
# 
# lm_eqn <- function(df111){
#     m <- lm(y ~ x, df111);
#     eq <- substitute(y == a + b %.% x*","~~r^2~"="~r2,
#          list(a = format(unname(coef(m)[1]), digits = 2),
#               b = format(unname(coef(m)[2]), digits = 2),
#              r2 = format(summary(m)$r.squared, digits = 3)))
#     as.character(as.expression(eq));
# } 
# p2 <- p111 +
#   geom_text(x = 25, y = 300,
#             label = lm_eqn(df111),
#             parse = TRUE)
# p2
# 
# 
# lm_eqc <- function(plan_wide_19902020){
#    m <- lm(`Oxigênio dissolvido` ~ CODIGO, plan_wide_19902020);
#    eq <- substitute(y == a + b %.% x*","~~r^2~"="~r2,
#                     list(a = format(unname(coef(m)[1]), digits = 2),
#                          b = format(unname(coef(m)[2]), digits = 2),
#                          r2 = format(summary(m)$r.squared, digits = 3)))
#    as.character(as.expression(eq));
# }
# 
# (od_p1 <-ggplot(plan_wide_19902020 %>%
#                    filter(ANO_COLETA>"1990" &
#                              ANO_COLETA<="2000"),
#                 aes(CODIGO,
#                     `Oxigênio dissolvido`))+
#       annotate("rect",
#                xmin=-Inf,
#                xmax=Inf,
#                ymin=-Inf,
#                ymax=2,
#                alpha=1,
#                fill="#ac5079")+ #>pior classe
#       annotate("rect",
#                xmin=-Inf,
#                xmax=Inf,
#                ymin=2,
#                ymax=4,
#                alpha=1,
#                fill="#eb5661")+ #classe 4
#       annotate("rect",
#                xmin=-Inf,
#                xmax=Inf,
#                ymin=4,
#                ymax=5,
#                alpha=1,
#                fill="#fcf7ab")+ #classe 3
#       annotate("rect",
#                xmin=-Inf,
#                xmax=Inf,
#                ymin=5,
#                ymax=6,
#                alpha=1,
#                fill="#70c18c")+ #classe 2
#       annotate("rect",
#                xmin=-Inf,
#                xmax=Inf,
#                ymin=6,
#                ymax=Inf,
#                alpha=1,
#                fill="#8dcdeb")+ #classe 1
#       stat_boxplot(geom = 'errorbar',
#                    width=0.3,
#                    position = position_dodge(width = 0.65))+
#       geom_boxplot(fill='#F8F8FF',
#                    color="black",
#                    outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
#                    width= 0.7)+
#       labs(title = "Oxigênio Dissolvido no período 1990-2000",
#            x="Estação",
#            y="mg/L")+
#       # geom_jitter(width = .05,
#       #             alpha=.2,
#       #             size=1.5,
#       #             color="black")+
#       scale_y_continuous(expand = expansion(mult = c(0,0)),
#                          n.breaks = 11,
#                          limits = c(-1,21))+
#       scale_x_discrete(limits = c("87398500", "87398980", "87398900", "87398950", "87405500", "87406900", "87409900"))+
#       geom_smooth(method = "lm",
#                   se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
#                   aes(group=1),
#                   alpha=.5,
#                   na.rm = TRUE,
#                   size = 1)+
#       # annotate(geom_text(aes(x = "87405500", y = 15),
#       #                    label = lm_eqc(plan_wide_19902020),
#       #                    parse = TRUE,
#       #                    inherit.aes = TRUE,
#       #                    check_overlap = TRUE))+
#       #  geom_line(
#       #     aes(color="red"),
#       #     alpha=.0,
#       # )+
#       # scale_color_manual("Legenda",
#       #                    guide="legend",
#       #                    values = c("Classe 1"="#8dcdeb",
#       #                               "Classe 2"="#70c18c",
#       #                               "Classe 3"="#fcf7ab",
#       #                               "Classe 4"="#eb5661",
#       #                               "Pior Classe"="#ac5079"))+
#    # guides(color=guide_legend(override.aes = list(linetype=c(1,1,1,1,1),
#    #                                               lwd=c(2,2,2,2,2),
#    #                                               shape=c(NA,NA,NA,NA,NA),
#    #                                               alpha=1)))+
#       theme(legend.position = "bottom")+
#       theme_classic())

# list1111 <- sessionInfo()
# list1111$loadedOnly

# install.packages("ggpmisc")
# library(ggpmisc)

# summary(lm(plan_wide_19902020$CODIGO~plan_wide_19902020$DBO))
# 
# 
# p <- ggplot(data, aes(y=number, x=pod)) +
#   geom_boxplot()
# print(p)

# install.packages("GGally")


# fit = lm(plan_wide_19902020$`Oxigênio dissolvido`~ plan_wide_19902020$CODIGO)
# summary(fit)
# summary.lm(fit)
# 
# plan_wide_19902020$IQA
# 
# plan_wide_19902020 <- plan_wide_19902020 %>% 
#    mutate(IQA = ifelse(IQA == 0, NA, IQA))
# pacman::p_load(esquisse)
```

### Correlação
```{r Correlação, time_it = TRUE}
parametros_IQA <- plan_wide_19902020 %>%
  select(CODIGO,
         pH,
         DBO,
         `Nitrogênio amoniacal`,
         `Nitrogênio total`,
         `Fósforo total`,
         `Temperatura água`,
         Turbidez,
         `Sólidos totais`,
         `Oxigênio dissolvido`,
         Condutividade)

write.csv(parametros_IQA,
          "./parametros_IQA.csv",
          row.names = FALSE)

parametros_IQA %>% 
  select(-CODIGO) %>% 
  ggcorr(method = "complete.obs",
           # "pearson",
           # "pairwise",
         name = "Correlação",
         label = TRUE,
         label_alpha = TRUE,
         digits = 3,
         low = "#3B9AB2",
         mid = "#EEEEEE",
         high = "#F21A00",
         # palette = "RdYlBu",
         layout.exp = 0,
         legend.position = "left",
         label_round = 3,
         )

# Gráfico das correlações entre todos os parâmetros com significância
# correl_IQA <- parametros_IQA %>% 
#   select(-CODIGO) %>% 
#   ggpairs(title = "Correlação entre parâmetros que compõem o IQA",
#           axisLabels = "show")
```

### Condutividade elétrica

```{r Gráfico cond_elet periodo1, warning = FALSE, message = FALSE}
(cond_elet_p1 <- ggplot(plan_wide_19902020 %>% 
                          filter(ANO_COLETA>"1990" &
                                   ANO_COLETA<="2000"),
                        aes(CODIGO,
                            Condutividade))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=500,
            ymax=Inf,
            alpha=1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=-Inf,
            ymax=500,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "Condutividade elétrica no período 1990-2000",
        x="Estação",
        y="")+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_y_continuous(expand = expansion(mult = c(0.01, 0.05)),
                      n.breaks = 8,
                      limits = c(0,
                                 max(plan_wide_19902020$Condutividade, na.rm = TRUE)),
                      labels = scales::number_format(accuracy = 1,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
   scale_x_discrete(limits = c("87398500", "87398980", "87398900",
                               "87398950", "87405500", "87406900", "87409900"))+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme(
     plot.title = element_text(
       hjust = 0.5,
       color = "black",
       size = 19),
     axis.title.y = element_text(
       color = "black",
       size = 15),
     axis.text.y = element_text(
       color = "black",
       size = 17),
     axis.text.x = element_text(
       color = "black",
       size = 17),
   )
)
```

```{r Gráfico cond_elet periodo2, warning = FALSE, message = FALSE}
(cond_elet_p2 <- ggplot(plan_wide_19902020 %>% 
                          filter(ANO_COLETA>"2000" &
                                   ANO_COLETA<="2010"),
                        aes(CODIGO,
                            Condutividade))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=500,
            ymax=Inf,
            alpha=1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=-Inf,
            ymax=500,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "Condutividade elétrica no período 1990-2000",
        x="Estação",
        y="")+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_y_continuous(expand = expansion(mult = c(0.01, 0.05)),
                      n.breaks = 8,
                      limits = c(0,
                                 max(plan_wide_19902020$Condutividade, na.rm = TRUE)),
                      labels = scales::number_format(accuracy = 1,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
   scale_x_discrete(limits = c("87398500", "87398980", "87398900", 
                               "87398950", "87405500", "87406900", "87409900"))+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme(
     plot.title = element_text(
       hjust = 0.5,
       color = "black",
       size = 19),
     axis.title.y = element_text(
       color = "black",
       size = 15),
     axis.text.y = element_text(
       color = "black",
       size = 17),
     axis.text.x = element_text(
       color = "black",
       size = 17),
   )
)
```

```{r Gráfico cond_elet periodo3, warning = FALSE, message = FALSE}
(cond_elet_p3 <- ggplot(plan_wide_19902020 %>% 
                          filter(ANO_COLETA>"2010" &
                                   ANO_COLETA<="2020"),
                        aes(CODIGO,
                            Condutividade))+
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=500,
            ymax=Inf,
            alpha=1,
            fill="#eb5661")+ #classe 4
   annotate("rect",
            xmin=-Inf,
            xmax=Inf,
            ymin=-Inf,
            ymax=500,
            alpha=1,
            fill="#8dcdeb")+ #classe 1
   stat_boxplot(geom = 'errorbar',
                width=0.3,
                position = position_dodge(width = 0.65))+
   geom_boxplot(fill='#F8F8FF',
                color="black",
                outlier.shape = 1, #se deixar NA fica só o jitter, se não, deixa 1
                width= 0.7)+
   labs(title = "Condutividade elétrica no período 1990-2000",
        x="Estação",
        y="")+
   ggbeeswarm::geom_quasirandom(
     size = 1.2,
     alpha = .25,
     width = .07,
   )+
   scale_y_continuous(expand = expansion(mult = c(0.01, 0.05)),
                      n.breaks = 8,
                      limits = c(0,
                                 max(plan_wide_19902020$Condutividade, na.rm = TRUE)),
                      labels = scales::number_format(accuracy = 1,
                                                     decimal.mark = ",",
                                                     big.mark = " "))+
   scale_x_discrete(limits = c("87398500", "87398980", "87398900",
                               "87398950", "87405500", "87406900", "87409900"))+
   geom_smooth(method = "lm",
               se=FALSE, #se deixar TRUE gera o intervalo de confiança de 95%
               aes(group=1),
               alpha=.5,
               na.rm = TRUE,
               size = 1)+
   theme(
     plot.title = element_text(
       hjust = 0.5,
       color = "black",
       size = 19),
     axis.title.y = element_text(
       color = "black",
       size = 15),
     axis.text.y = element_text(
       color = "black",
       size = 17),
     axis.text.x = element_text(
       color = "black",
       size = 17),
   )
)
```

```{r Gráfico cond_elet 3 periodos juntos, warning=FALSE, message=FALSE}
grid.arrange(cond_elet_p1, cond_elet_p2, cond_elet_p3, ncol = 3)
```

```{r Sumário cond_elet}
(sum_cond_elet_p1 <- plan_wide_19902020 %>%
   select(CODIGO, Condutividade, ANO_COLETA) %>% 
   filter(ANO_COLETA>"1990" &
            ANO_COLETA<="2000") %>% 
   group_by(CODIGO) %>% 
   summarize(
     min = 
       min(Condutividade, 
           na.rm = TRUE),
     q1 = 
       quantile(Condutividade, 0.25, 
                na.rm = TRUE),
     median = 
       median(Condutividade, 
              na.rm = TRUE),
     mean = 
       mean(Condutividade, 
            na.rm= TRUE),
     q3 = 
       quantile(Condutividade, 0.75, 
                na.rm = TRUE),
     max = 
       max(Condutividade, 
           na.rm = TRUE))
)

(sum_cond_elet_p2 <- plan_wide_19902020 %>%
    select(CODIGO, Condutividade, ANO_COLETA) %>% 
    filter(ANO_COLETA>"2000" &
             ANO_COLETA<="2010") %>% 
    group_by(CODIGO) %>% 
    summarize(
      min = 
        min(Condutividade, 
            na.rm = TRUE),
      q1 = 
        quantile(Condutividade, 0.25, 
                 na.rm = TRUE),
      median = 
        median(Condutividade, 
               na.rm = TRUE),
      mean = 
        mean(Condutividade, 
             na.rm= TRUE),
      q3 = 
        quantile(Condutividade, 0.75, 
                 na.rm = TRUE),
      max = 
        max(Condutividade, 
            na.rm = TRUE))
)

(sum_cond_elet_p3 <- plan_wide_19902020 %>%
    select(CODIGO, Condutividade, ANO_COLETA) %>% 
    filter(ANO_COLETA>"2010" &
             ANO_COLETA<="2020") %>% 
    group_by(CODIGO) %>% 
    summarize(
      min = 
        min(Condutividade, 
            na.rm = TRUE),
      q1 = 
        quantile(Condutividade, 0.25, 
                 na.rm = TRUE),
      median = 
        median(Condutividade, 
               na.rm = TRUE),
      mean = 
        mean(Condutividade, 
             na.rm= TRUE),
      q3 = 
        quantile(Condutividade, 0.75, 
                 na.rm = TRUE),
      max = 
        max(Condutividade, 
            na.rm = TRUE),
      n = 
        length(Condutividade))
)

# plan_wide_19902020 %>% 
#    select(CODIGO, IQA) %>% 
#    group_by(CODIGO) %>% 
#    summarize(
#       min = 
#          min(IQA, 
#              na.rm = TRUE),
#       q1 = 
#          quantile(IQA, 0.25, 
#                   na.rm = TRUE),
#       median = 
#          median(IQA, 
#                 na.rm = TRUE),
#       mean = 
#          mean(IQA, 
#               na.rm= TRUE),
#       q3 = 
#          quantile(IQA, 0.75, 
#                   na.rm = TRUE),
#       max = 
#          max(IQA, 
#              na.rm = TRUE))
```

```{r Salvando cond_elet}
ggsave("cond_elet_p1.png",
       plot = cond_elet_p1,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("cond_elet_p2.png",
       plot = cond_elet_p2,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("cond_elet_p3.png",
       plot = cond_elet_p3,
       path = "./graficos",
       dpi = 300,
       type = "cairo")

ggsave("cond_elet_3periodos.png",
       units = c("px"),
       width = 4500,
       height = 2993,
       plot = grid.arrange(cond_elet_p1, cond_elet_p2, cond_elet_p3, ncol = 3),
       path = "./graficos",
       dpi = 300,
       type = "cairo")

```

